A-SLOTH Documentation

The semi-analytical model A-SLOTH (Ancient Stars and Local Observables by Tracing Halos) is the first public code that connects the formation of the first stars and galaxies to observables. The model is based on dark matter merger trees that can either be generated based on Extended Press-Schechter theory or that can be imported from dark matter simulations. On top of these merger trees, A-SLOTH applies analytical recipes for baryonic physics to model the formation of both metal-free and metal-poor stars and the transition between. A-SLOTH samples individual stars and includes radiative, chemical, and mechanical feedback. It is calibrated based on six observables, such as the optical depth to Thomson scattering, the stellar mass of the Milky Way and its satellite galaxies, the number of extremely-metal poor stars, and the cosmic star formation rate density at high redshift. A-SLOTH has versatile applications with moderate computational requirements. It can be used to constrain the properties of the first stars and high-z galaxies based on local observables, predicts properties of the oldest and most metal-poor stars in the Milky Way, can serve as a subgrid model for larger cosmological simulations, and predicts next-generation observables of the early Universe, such as supernova rates or gravitational wave events. More details on the astrophysical models can be found in our ApJ paper.

Getting Started

You can clone A-SLOTH to your machine with:

git clone https://gitlab.com/thartwig/asloth.git

If you encounter a problem or error at any point, you can consult the section on debugging, which contains a list of known problems and their solutions.

System Requirements and Dependencies

To run A-SLOTH you need a fortran compiler. We tested recent ifort and gfortran compilers, but you are welcone to try different ones. Set the correct compiler in the Makefile. A-SLOTH uses some features of the 2003 and 2008 standards of fortran, so very old compilers will probably not work. You will also need an Open-MP library if you want to run the code in parallel and python 3 and if you indend to use our plotting scripts. The Code is usually using a large amount of RAM. The fiducial EPS-based mode required about 4GB of RAM. We recommend 24-64 GB of RAM for modelling a full Milky Way based on the Caterpillar merger trees.

We have tested A-SLOTH and the provided tutorials on the following systems:

Ubuntu 18.04, gfortran 7.5.0 / gfortran 10.3.0 / ifort 2021.5.0, python 3.8.3
Ubuntu 20.04, gfortran 9.4.0, python 3.8.2
CentOS Linux 7, ifort 2019.2, python 3.8.9
MacOSX 10.14.6, gfortran 11.2.0 / gfortran 8.2.0, python 3.9.9 / python 3.6.13 / python 3.7.3
MacOSX 10.15.7, gfortran 9.2.0, python 3.7.10

To execute the python analysis scripts, you might need to install the additional python packages (for example with pip3 install PackageName)

numpy
scipy
matplotlib
pandas
psutil
astropy

Tutorials

The following tutorials will guide you through the basic functions and applications of A-SLOTH. We recommand to complete these tutorials in order. A walk-through for the tutorials is provided on YouTube. The tutorials are based on the public release version of A-SLOTH with no other modifications. If a tutorial does not work, you might want to first reset A-SLOTH to the fiducial version, i.e., by checking git status, and by running make clean. Or you might need to first delete or rename previous output folders in the A-SLOTH directory. If not stated otherwise, all commands should be executed from the main A-SLOTH directory.

All python scripts in these tutorials should be run with python 3.

Tutorial 1: Hello A-SLOTH

Here, we summarize how you can run A-SLOTH with fiducial settings and check your outputs with standard plotting scripts. Change to the A-SLOTH folder and compile the code with

cd asloth/
make

Then you can run A-SLOTH with the command

./asloth.exe param_template.nml

If everything goes well, you should then see the statement

  ...
 Starting main star formation and feedback loop
 Current redshift:   0.0 PROGRESS: [###############################]100.00% -- Complete
 A-SLOTH finished successfully.

Now, we can have a look at the results by running python-based plotting scripts:

python scripts/plot_asloth.py

Ideally, the script will use the newest folder to visualize the results. It will also tell you in which folder the plots are saved:

...
The directory output_DATE_TIME exists and we will use it to create plots.
Plots will be saved in output_DATE_TIME/plots/
Adopted EMP fraction in MW: 2.314815e-05
...

The resulting figures should look like this:

alt text

or this:

alt text

Please note: A-SLOTH is only deterministic if the same random seed, compiler, processor architecture, and optimisation flags are used. Therefore, your simulation output might look slightly different than this plot. As reference, we provide two additional reference values (SEED0, SEED1) with different random seeds to give you an idea how much the random seed can affect the results.

Tutorial 2: Vary one input parameter manually

In this tutorial, we investigate the effect of the Pop III IMF on the MDF. The fiducial model suggests Mmax=210Msun. Let’s see how observables change if we modify this fiducial value to 100Msun or 300Msun (note that the mass range of PISNe is about 140-260Msun). This tutorial consists of two steps: first, we run the 3 different models (Mmax = [100,210,300]Msun). Then we will plot the MDFs from these 3 models

Step 1: Run the three models. To run the fiducial model with Mmax=210Msun, you need to run:

make clean
make
./asloth.exe param_template.nml

Once A-SLOTH has finished, you should have one folder named output_YYYYMMDD_HHMMSS. For better overview, you can rename this folder (not necessary, but helps later):

mv output_day_time output_Mmax210

Now, we want to run a second model with a different value for Mmax. For this purpose, we first copy the parameter file:

cp param_template.nml param_Mmax.nml

Then, we open the file param_Mmax.nml and modify the line with

IMF_max=210 ! maximum mass of PopIII IMF

to the desired value, let’s say 100, and save the parameter file. Now we can run A-SLOTH again with this new parameter file:

./asloth.exe param_Mmax.nml

This should have generated another output folder that you can rename again. Now you can change IMF_max in param_Mmax.nml again and run A-SLOTH a third time. At the end of this step, you should have three folders with the results of the three runs that are named

output_Mmax100
output_Mmax210
output_Mmax300

Step 2: Plot the results. Open the file scripts/plot_asloth.py and scroll to the bottom. Change UserFolder to

UserFolder = ["output_Mmax100", "output_Mmax210", "output_Mmax300"]

and UserLabels to

UserLabels = ["Mmax=100Msun", "Mmax=210Msun", "Mmax=300Msun"]

Furthermore, set plot_newest_folder = False. Save the file and run it with

python scripts/plot_asloth.py

The python scripts should have told you

...
Plots will be saved in output_Mmax100/plots/
...

(together with many other information). So let’s look into this folder, which should contain the file MW_MDF.pdf, which should look like this (due to different random number generators or seeds, the figure might not look exactly like this one): alt text

We can see that the run with Mmax=100Msun (blue) differs from the other runs in the metallicity range below [Fe/H]=-3. Congratulations! You now know how to run and compare A-SLOTH with different input parameters manually.

Tutorial 3: Explore two input parameters automatically

In this tutorial we want to use A-SLOTH for a parameter exploration and see which observables are most sensitive to the details of PopIII star formation. Specifically, we want to vary the upper mass limit of the PopIII IMF and the PopIII star formation efficiency. Therefore, we run many realizations and sample random pairs of these two parameters. At the end, we visualize the effect of these parameters on the observables. Optional: you can set the variable path_output in scripts/wrapper/loop_trees.py and the variable folder_name in scripts/wrapper/analyse_trees.py to a folder in which you want to save the output. Otherwise, output will be written to your A-SLOTH folder. The following script will run for 15-30Min:

python scripts/wrapper/loop_trees.py

This script launches a python wrapper which will run 32 x 3 EPS merger trees:

Draws random values for Mmax and ETAIII
Checks if enough RAM and CPUs are available
Launches first tree
If sufficient resources are available, it also launches a second and third EPS tree with the same input parameters, but with a different random seed.
It continues with 1)-4) until either 32 x 3 trees are completed OR until 30Min are over.
If sufficient resources are not available (RAM, CPUs, disk I/O), it will wait a few seconds and try again.

Please note: the output to terminal might seem confusing because multiple trees are running at the same time. If the python script finishes before A-SLOTH, you might not get back to the command line prompt at the end of this run. If this is the case, you can press Enter once the last tree has finished.

Once this script has finished, you should have many folders with the results in your path_output directory. In addition, the two figures RAM_histogram.pdf and CPU_histogram.pdf show you the available resources during the run. This is useful to check if any of these poses a bottleneck to the calculation. We have prepared another script to extract, digest, and compare the data of the output folders:

python scripts/wrapper/analyse_trees.py

This script goes through all the folders, collects the input parameters from each run, calculates the observables from A-SLOTH, and eventually compares them to real observables. During the run, plots are created in the output folders. The print messages will tell you in which folders you can find the individual plots. At the end, this script will produce the file Parameter_Exploration.dat. You can either look at this file manually, analyse it with your favourite software, or use a third script to visualize it:

python scripts/wrapper/tutorial_GridPlot.py

The generated figure Parameter_Exploration_tutorial.pdf should look like this cut-out: alt text

Tutorial 4: Analyse evolution of baryons in one merger tree branch

In this tutorial, we want to activate one additional compile time option in order to create a plot like this: alt text This plot shows the evolution of different baryonic quantities as function of cosmic time for one branch of the merger tree. Your plot might look slightly different depending on the random seed. For another example and explanations of the illustrated quantities, see Fig. 3 in Hartwig+22.

To generate the output for this plots, we have to activate the compiler flag OUTPUT_GAS_BRANCH. To do this, open the file src/asloth.h and activate the option by remeoving the first ! in that line.

#define OUTPUT_GAS_BRANCH

You should recompile code with

make clean
make

Then, you can run the code with

./asloth.exe param_template.nml

In your output_date_time folder you should now have the additional file t_gas_substeps.dat. To illustrate these results, you can use a python script. But first, we have to set the output folder in the script. Open the file scripts/utilities/plot_tgas.py and in the very last line change output_folder to your output folder that was just created. Save the file and run

python scripts/utilities/plot_tgas.py

This should have generated the file t_gas.pdf in your output folder.

Tutorial 5: Output additional information from A-SLOTH

So far, we have used output files that were already implemented and written to the output folder. Now, we want to learn how users can implement their own desired output to a file. There are various options to write to a file. One can either keep a file open and write to it incrementally during the run. Or one can write all information to a file at once at the end of the run. In this example, we will look at the subroutine write_files_reali() in the file src/to_file, which is called at the end of the run to write outputs to files. In this file, you will find a section that starts with

!!! TUTORIAL 5: START !!!

At the moment, this code opens a file, writes a header, and closes the file. The do-loop iterates over all dark matter halos in this run and the write statement would write information for every halo. Such a file would be huge with millions of lines. Hence, it is currently deactivated by

if(.False.)then

This routine should write the redshift and stellar mass of halos. Let’s say we want this information only for PopIII-dominated halos, i.e., for halos that contain more PopIII stars than PopII stars.

Change the if-statement so that we only write information of PopIII-dominated halos to the file. Yes, you have to think how this if-statement looks like and can not simply copy it from the handbook as in previous tutorials. When you need additional information what the different node properties mean (Node%quantity), you can look in the file src/defined_types.F90 under type TreeNode. Save the file once you have changed the if-statement. Then you can compile and run the code, as you have done in Tutorial 1. Again, you should get a new folder output_date_time.

To illustrate the results, you can open the python script scripts/utilities/tutorial5.py, set the correct output_folder, save it, and run it with

python scripts/utilities/tutorial5.py

alt text

Please note that the plot shows cumulative stellar masses, i.e., stellar masses that have formed until a certain redshift, but it does not account for stars that have already died based on their lifetime. While this plot is not immensly informative, this tutorial has shown you how to output and illustrate additional quantities from A-SLOTH. Happy coding!

Configure your simulation

The following files are relevant to change the general behaviour of the code:

Parameter file. We provide the parameter file param_template.nml. We recommend to first create a copy of this file if you want to change these values. Numerical values in this file are calibrated to match observations, see Hartwig+22.
Config file. You can find the available compile time options in the file src/asloth.h. You have to recompile the code if you change these compiler time options.
Numerical parameters. You can find additional parameters in the file src/num_pars.F90. They are set to recommended values. You might want to change some of these parameters, such as the simulated redshift range, or the halos mass resolution.

How Tos

Debugging

If A-SLOTH is not behaving as expected, this section might help. If the code is not compiling, please check the code requirements. A-SLOTH required several GB of RAM. Please check your available RAM before and during the run with top or free -m. If the code is running but crashes (e.g. segmentation fault) we recommend external debugging software such as valgrind. If the code is running, but the results seem strange to you, you can try the options below. If you think you found a bug, please report it via the GitLab issue tracker or by e-mail to hartwigATphys.s.uMINUStokyo.ac.jp.

Compiler Options

In the file src/asloth.h you can activate the compile time options DEBUG, INFO, and MEM_REPORT, which will provide additional outputs, especially if something is strange.

Python Scripts

The following python functions can be useful to identify problems with the code:

scripts/plot_asloth.py
scripts/plot_tgas.py

Additional Output

You can print additional information to the terminal or a file to check if variables during the run have the values that you expect. We provide the subroutine print_all_props() to print additional information of nodes (see file src/defined_types.F90).

Common Issues

Git clone does not work

If you get the error SSL_ERROR_SYSCALL in connection to gitlab.com:443 or similar, you can also manually download the source code from the repository website.

The code does not compile

This can occur when you modified the ‘asloth.h’ or changed how you compile (e.g. the compiler and/or optimization flags) in the middle of compiling. It may also be helpful recompile the code entirely with

make clean
make

If the reason for being unable to compile is that the compiler is unable to find a module, please re-generate the dependency list via

python scripts/utilities/make_dependency_list.py

The generation of the dependency list fails

If the generation of the dependency list gets stuck and encounters a stack overflow after a while, most likely a circular dependency has been introduced. Be aware that if module A uses module B, neither module B nor any module that uses it can use module A

A-SLOTH does not accept my parameter.nml file

This file contains a set of fortran namelists. All namelists need to be present, even if they are empty. The names of all given parameters need to be identical to the names of the parameters in the specified namelist in the code. They also must have the correct type, and - if its an array - length. Missing quotation marks for a string or superflous quotation marks for a non-string variable are a likely reason.

The code crashes with a strange memory error

Make sure you compile with the options -fbounds-check (gfortran) or -CB (ifort). The most common reason for such memory errors is stepping out of bounds of an array. If the error only occurs with ifort but not gfortran, make sure to use the -heap-arrays flag for compiling. Compiling with -g (if you do not do so already) will give you slightly improved debugging information. If you recieve a segmentation fault while writing a very long line to a text file, try increasing the recl parameter when opening the offending file.

Compilation with ifort crashes

If your compilation crashes with

ifort -c  src/stellar/IMF.F90 -O3 -fpp -xHost -ipo -heap-arrays -g -r8 -D IFORT -shared-intel -module mods -o build/stellar/IMF.o
src/stellar/IMF.F90(53): warning #6178: The return value of this FUNCTION has not been defined.   [RES]
  function P_func_dummy(this, m1, m2) result(res)
---------------------------------------------^

you can try to use a newer version of the ifort compiler or the gfortran compiler instead.

A-SLOTH crashes with end of file error

At line 46 of file src/cosmic_time.F90
Fortran runtime error: End of file

Make sure that the value of nlev is correct in the parameter file.

The standard python plotting script does not work

If you try to plot output and it crashes with this error

asloth$ python scripts/plot_asloth.py
Traceback (most recent call last):
  File "scripts/plot_asloth.py", line 156, in <module>
    MyFit = plot_comparison(UserFolder, UserLabels=UserLabels)
  File "scripts/plot_asloth.py", line 51, in plot_comparison
    from utilities.utility import asloth_fit
ImportError: No module named utilities.utility

or similar, you can try to run it explicitely with python3 (you should always run the plotting scripts with python3).

Changing which chemical elements are traced

If you want to trace different elements, you can specify these in the parameter file under ElementName. You should then also update N_ELEMENTS so that N_ELEMENTS = len(ElementName)+1.

Get a tree

The code can be run in the EPS mode without any additional merger tree data. However, we recommend the EPS mode only for testing. To obtain reliable results, one should obtain dark matter merger trees from simulations. A-SLOTH uses its own binary format. Please contact the team on advice on how to obtain trees. The code includes a sample-script to convert merger trees from consistent-trees to our own format (reindex/reindex.F90, please look at the comments in the source code for instructions on how to use it).

Check Changes

If you have modified the source code and want to check the resulting differences, you can use the provided python scripts to do so. Let’s say you ran the fiducial version of A-SLOTH and saved the output in the folder output_v0. Then you implement some new physics or improve some routine, run the code again and obtain a new output folder with the name output_v1.

You can visually compare the output of these two folders with the function scripts/plot_asloth.py. You should set

UserFolder = ["output_v0","output_v1"]
UserLabels = ["v0","v1"]
plot_newest_folder = False

and run it with python3 scripts/plot_asloth.py. You can use the produced plots to inspect the changes from your code update.

References and Links

Articles by the ASLOTH collaboration

Hartwig et al. 2022
Magg et al. 2022
Chen et al. 2022
Tarumi et al. 2020
Hartwig et al. 2018
Magg et al. 2018
Magg et al. 2016
Hartwig et al. 2016b
Hartwig et al. 2016a
Hartwig et al. 2015

The Caterpillar Project

Caterpillar Website

List of Modules and Procedures

Main Code

ASLOTH main program

program asloth

Use: config, defined_types, eps_wrapper, tree_memory_arrays_passable, numerical_parameters, resolution_parameters, to_file, input_files, check_flags, utility, volume_fractions, feedback_routines (node_feedback()), bubble_tree (start_btree()), tree_reader, tree_initialization, star_formation, read_yields, metal_functions, populations, sf_routines, crit_masses (set_mcrit()), cosmic_time (set_cosmic_time())
Call to: set_default_config(), read_config(), check_compiler_flags(), init_outputs(), set_cosmic_time(), set_mcrit(), read_tree(), make_eps_tree(), init_stellar_populations(), assign_yields(), output_pop(), start_btree(), init_tree(), print_bar(), real_to_string3(), mem_report(), clean_tree(), get_fracs(), node_feedback(), sf_step(), add_parent_to_tree(), add_to_sfr(), add_to_feedback_volumes(), add_to_base_mdf(), add_to_base_mstariisurv(), write_files_reali(), outputs_satellites(), write_mw_properties(), write_files(), close_outputs()

Configuration

config

Quick access

Variables: config_file, cubic_box, nthreads
Routines: read_config(), set_default_config()

Needed modules

numerical_parameters
input_files: List of input files
random_object: This is an object oriented version of the ran3 algorithm from numerical recipes Before using a rng type object it needs to be initialized by calling rng%init(seed) where seed is an integer seed Afterwards rng%get_ran() will return a random real in the range [0,1)

Variables

config/config_file [character,protected]

e.g. param_template.nml
config/cubic_box [logical,protected]
config/nthreads [integer,protected]

Subroutines and functions

subroutine config/set_default_config()

Called from: asloth

subroutine config/read_config(fname)

Parameters: fname [character,in]
Use: omp_lib
Called from: asloth

Type definitions

defined_types

Quick access

Types: node_arrays, stars_highmass, treenode
Routines: add_baryons(), del_high_mass(), make_stars_high_mass(), print_all_props(), set_baryons()

Needed modules

metals (n_elements()): main module for handling metals
utility: General purpose utility functions

Types

type defined_types/treenode
class for storing information of each halo at each time
Type fields
% base [treenode,pointer] :: which halo will this one be at at the the lowest redshift

% child [treenode,pointer/optional/default=>null()]

% desc_id [integer] :: descendent id

% firstpopiisf [logical,optional/default=.true.]

% highstars [stars_highmass,pointer/optional/default=>]

% highstars_first [stars_highmass,pointer/optional/default=>]

% highstars_last [stars_highmass,pointer/optional/default=>]

% i_checked [logical] :: has this halo already been checked for stochastic feedback?

% i_enr [logical,optional/default=.false.] :: is this halo enriched?

% i_ion [logical,optional/default=.false.] :: is the halo affected by ionising radiation

% i_mcrit [logical,optional/default=.false.] :: is the halo above the critical mass?

% i_sf [logical,optional/default=.false.] :: this flag controls whether the SF_step subroutine is called

% i_sub [logical,optional/default=.false.] :: is this a subhalo?

% id [integer] :: halo id

% igm_z (*) [real,allocatable] :: ambient metallicity

% jlevel [integer] :: which time-step is the halo at

% l_ion [real] :: ionising radiation emission rate [Photons/second]

% llstars [stars_highmass,pointer/optional/default=>]

% llstars_first [stars_highmass,pointer/optional/default=>]

% llstars_last [stars_highmass,pointer/optional/default=>]

% lp_id [integer] :: last progenitor depthfirst id

% m_cold [real] :: cold gas mass [M_sun]

% m_filter [real,optional/default=0]

% m_hot [real] :: hot gas mass [M_sun]

% m_metals (,) [real,allocatable]

% m_out [real] :: outflow mass [M_sun]

% m_peak [real] :: peak mass [Msun]

% m_peak_filter [real,optional/default=0]

% m_starii [real] :: stellar mass Pop II [M_sun]

% m_starii_surv [real,optional/default=0.0] :: Msun

% m_stariii [real] :: stellar mass Pop III [M_sun]

% mhalo [real] :: mass of halo

% nchild [integer] :: number of child nodes

% null [node_arrays,pointer]

% num_popiiprogenitor [integer,optional/default=0] :: how many Pop II forming progenitors

% parent [treenode,pointer/optional/default=>null()]

% pop [integer]

% r_en [real,optional/default=0] :: enrichment radius [physical Mpc]

% r_ion [real,optional/default=0] :: ionised radius [physical Mpc]

% sibling [treenode,pointer/optional/default=>null()]

% this_array [node_arrays,pointer/optional/default=>]

% x (3) [real] :: position
type defined_types/node_arrays
Type fields
% mdf_array (,) [real,allocatable]
type defined_types/stars_highmass
Type fields
% bin_id [integer,optional/default=0] :: which bin it belongs, should be consistent with IMF arrays

% next [stars_highmass,pointer/optional/default=>]

% null [stars_highmass,pointer]

% num [integer,optional/default=0]

% pop [integer,optional/default=0] :: PopII (2) or PopIII (3) stars

% prev [stars_highmass,pointer/optional/default=>]

% timeborn [real,optional/default=0.0] :: in order to know when the stars die

% z (*) [real,allocatable] :: Zgas [Z/Zsun] when the star is born. Related with SN yields.

Subroutines and functions

function defined_types/make_stars_high_mass(n, i, timeborn, zgas, pop)

Parameters

n [integer,in]
i [integer,in]
timeborn [real,in]
zgas (*) [real,in]
pop [integer,in]

Return

res [stars_highmass]

subroutine defined_types/del_high_mass(this)

Parameters: this [stars_highmass,inout]

subroutine defined_types/set_baryons(this, other)

copies baryonic properties from one halo to another (overwrites)

Parameters

this [real]
other [real]

subroutine defined_types/add_baryons(this, other)

adds baryonic properties of one halo to another

Parameters

this [real]
other [real]

subroutine defined_types/print_all_props(a)

Printing function that prints various porperties of a halo in order to allow easier tracing of errors and unexpected changes

Parameters: a [treenode,in]

Tree Reader

tree_reader

Quick access

Routines: read_tree()

Needed modules

defined_types
input_files: List of input files
numerical_parameters
tree_memory_arrays: This module is use for managing the memory of the merger tree
tree_memory_arrays_passable: This module is used to reference the merger tree
utility: General purpose utility functions
cosmic_time

Subroutines and functions

subroutine tree_reader/read_tree(count, read_mass)

Parameters

count [integer,out] :: reading tree
read_mass [real,out]

Called from

asloth

Call to

print_bar(), mem_report()

Tree initialization

tree_initialization

Description

prepares tree for running asloth

Quick access

Routines: filter_virial_masses(), init_tree(), set_tree_params()

Subroutines and functions

subroutine tree_initialization/init_tree(count, n_jcurrent)

Parameters

count [integer,in]
n_jcurrent (nlev_max) [integer,out]

Use

defined_types, numerical_parameters, tree_memory_arrays_passable, utility, crit_masses, cosmic_time, resolution_parameters, metalmix_dz, filter_module, trace_mdf, metals

Called from

asloth

Call to

print_bar(), mem_report(), read_lookups(), filter_virial_masses(), init_mdf()

subroutine tree_initialization/filter_virial_masses()

Use: defined_types, numerical_parameters, tree_memory_arrays_passable, filter_module
Called from: init_tree()
Call to: compute_savgol_coeff(), print_bar(), savgol_filter(), savgol_free()

subroutine tree_initialization/set_tree_params()

Use: power_spectrum_parameters, modified_merger_tree, numerical_parameters, input_files, time_parameters
Called from: make_eps_tree()

numerical and resolution parameters

numerical parameters

Quick access

Variables: a_radiation, ab_mag_norm, ab_norm, alpha_b, alpha_ion, alpha_outflow, ang2m, atomic_mass_helium, atomic_mass_hydrogen, bt_delta, bt_max, bt_min, btnbin, btnpad, c_cgs, c_hii, c_light, c_light_angstroms, c_light_cm, clump, cm32m3, crit_freq, deca, delta200, deltaeds, e_51, em_rdisk_half_scale_o2, eps3, eps4, eps6, epsm, epsr, erg2j, escape_fraction, etaii, etaiii, ev2erg, ev2j, f_escii, f_esciii, fx_peak_nfw, g, g_cgs, g_gyrkms3, g_mpcgyr, g_mpcgyr2, g_si, gpi, gyr2s, gyr2yr, h0100, h0100pgyr, h_planck, h_planck_erg, hecto, hubble, hubble_cgs, imf_max, imf_min, iso_fac, j2erg, k_boltzmann, k_boltzmann_erg, khorizon, kilo, kind99, km2m, kms2mpcgyr, kom, kpc2cm, kstrc_1, kstrc_2, l_ab0, l_box, ln10, ln2, log4, log_lsun_erg, logang2m, logc_light, logc_light_angstroms, logdeca, logev2erg, loggyr2s, loghecto, logj2erg, logk_boltzmann, logkilo, logl_ab0, loglsun, loglsun_bc, logm2cm, logmega, logmpc2asat10pc, logpc2m, logpi, lograd2as, long_bn, lsun, lsun40_bc, lsun_bc, lsun_erg, lw_mode, m2cm, m32cm3, m8crit, m_atomic, m_atomic_g, m_be, m_cha_out, m_electron, mean_mol, mega, mend, milli, mp_cgs, mpc2asat10pc, mpc2cm, mpc2m, mpc_cgs, mpckm2gyr, mres, msolar, msolar_1030kg, msolar_g, mstart, msun_cgs, msun_mpc3_cgs, mu_primordial, myr2s, n_b_cgs, n_cloud_default, n_cold_default, n_h_cgs, n_ionii, n_ioniii, n_spec, nextoutputid, nlev, nlev_max, nstarmassbinii, nstarmassbiniii, omega_b, omega_l, omega_m, pc2cm, pc2m, pi, pi4, pio2, pio4, pisq, popii_slope, rad2as, rad2degrees, rdisk_half_scale, rho_b_ast, rho_b_cgs, rho_crit_cgs, rho_m_cgs, rhocrit, right_angle_degrees, semi_circle_degrees, sigma_8, sigma_t, sigma_thomson, slope, sqrt2, sqrt2opi, sqrt2pi, sqrtatomic_mass_hydrogen, sqrtdelta200, sqrtg, sqrtg_si, sqrtk_boltzmann, sqrtm_atomic, sqrtmpc2m, sqrtmsolar, sqrtpi, sqrtrhocrit, stdout_bn, surf_dens_norm_half, t_crit, t_ion, t_univ, tree_mass, v_com, v_enrich_popiii, v_out, vbc, x_hydrogen, x_hydrogen_solar, xh, y_helium, y_helium_solar, year_cgs, yhe, z_crit, z_metals, z_metals_solar

Variables

numerical_parameters/a_radiation [real,parameter=7.565743419463647e-16]
numerical_parameters/ab_mag_norm [real,parameter=48.6]
numerical_parameters/ab_norm [real,parameter=3.6307805477e-20]
numerical_parameters/alpha_b [real,parameter=2.6e-13]
numerical_parameters/alpha_ion [real,parameter=2.6e-13]
numerical_parameters/alpha_outflow [real]
numerical_parameters/ang2m [real,parameter=1e-10]
numerical_parameters/atomic_mass_helium [real,parameter=4.002602]
numerical_parameters/atomic_mass_hydrogen [real,parameter=1.00794]
numerical_parameters/bt_delta [real,optional/default=0.1]
numerical_parameters/bt_max [real,optional/default=log10(4.e17)]
numerical_parameters/bt_min [real,optional/default=log10(4.e15)]
numerical_parameters/btnbin [integer,optional/default=10]
numerical_parameters/btnpad [integer,optional/default=2]
numerical_parameters/c_cgs [real,parameter=29980000000.0]
numerical_parameters/c_hii [real,parameter=3]
numerical_parameters/c_light [real,parameter=299792458.0]
numerical_parameters/c_light_angstroms [real,parameter=2.99792458e+18]
numerical_parameters/c_light_cm [real,parameter=29979245800.0]
numerical_parameters/clump [real,parameter=4.0]
numerical_parameters/cm32m3 [real,parameter=1e-06]
numerical_parameters/crit_freq [real,parameter=3.969127815104263e-18]
numerical_parameters/deca [real,parameter=10.0]
numerical_parameters/delta200 [real,parameter=200.0]
numerical_parameters/deltaeds [real,parameter=177.65287922076283]
numerical_parameters/e_51 [real,parameter=1.2]
numerical_parameters/em_rdisk_half_scale_o2 [real,parameter=0.432067481]
numerical_parameters/eps3 [real,parameter=0.001]
numerical_parameters/eps4 [real,parameter=0.0001]
numerical_parameters/eps6 [real,parameter=1e-06]
numerical_parameters/epsm [real,parameter=0.0001]
numerical_parameters/epsr [real,parameter=1e-06]
numerical_parameters/erg2j [real,parameter=1e-07]
numerical_parameters/escape_fraction [real]
numerical_parameters/etaii [real]
numerical_parameters/etaiii [real]
numerical_parameters/ev2erg [real,parameter=1.60217733e-12]
numerical_parameters/ev2j [real,parameter=1.60217733e-19]
numerical_parameters/f_escii [real]
numerical_parameters/f_esciii [real]
numerical_parameters/fx_peak_nfw [real,parameter=0.2162165956]
numerical_parameters/g [real,parameter=4.301307710181788e-09]
numerical_parameters/g_cgs [real,parameter=6.674e-08]
numerical_parameters/g_gyrkms3 [real,parameter=4.205785220992724e-06]
numerical_parameters/g_mpcgyr [real,parameter=4.398999721935946e-12]
numerical_parameters/g_mpcgyr2 [real,parameter=4.498910530810333e-15]
numerical_parameters/g_si [real,parameter=6.67259e-11]
numerical_parameters/gpi [real,parameter=1.3212863952890194e-05]
numerical_parameters/gyr2s [real,parameter=3.15576e+16]
numerical_parameters/gyr2yr [real,parameter=1000000000.0]
numerical_parameters/h0100 [real,parameter=100.0]
numerical_parameters/h0100pgyr [real,parameter=0.10227121653079844]
numerical_parameters/h_planck [real,parameter=6.6260755e-34]
numerical_parameters/h_planck_erg [real,parameter=6.6260755e-27]
numerical_parameters/hecto [real,parameter=100.0]
numerical_parameters/hubble [real,parameter=0.6774]
numerical_parameters/hubble_cgs [real,parameter=2.1952879411478756e-18]
numerical_parameters/imf_max [real]
numerical_parameters/imf_min [real]
numerical_parameters/iso_fac [real,parameter=1.085736204755322]
numerical_parameters/j2erg [real,parameter=10000000.0]
numerical_parameters/k_boltzmann [real,parameter=1.3806503e-23]
numerical_parameters/k_boltzmann_erg [real,parameter=1.3806503e-16]
numerical_parameters/khorizon [real,parameter=0.00033356409519815205]
numerical_parameters/kilo [real,parameter=1000.0]
numerical_parameters/kind99 [integer,parameter=selected_real_kind(p,r=99)]
numerical_parameters/km2m [real,parameter=1000.0]
numerical_parameters/kms2mpcgyr [real,parameter=0.0010227121653079844]
numerical_parameters/kom [real,parameter=2.9434011554855882e-21]
numerical_parameters/kpc2cm [real,parameter=3.0856775807e+21]
numerical_parameters/kstrc_1 [real,parameter=0.6251543028]
numerical_parameters/kstrc_2 [real,parameter=1.191648617]
numerical_parameters/l_ab0 [real,parameter=43442114331434.14]
numerical_parameters/l_box [real]

(linear box size)
numerical_parameters/ln10 [real,parameter=2.302585093]
numerical_parameters/ln2 [real,parameter=0.6931471806]
numerical_parameters/log4 [real,parameter=0.602059991]
numerical_parameters/log_lsun_erg [real,parameter=33.5848963]
numerical_parameters/logang2m [real,parameter=-10.0]
numerical_parameters/logc_light [real,parameter=8.4768207]
numerical_parameters/logc_light_angstroms [real,parameter=18.476820699999998]
numerical_parameters/logdeca [real,parameter=1.0]
numerical_parameters/logev2erg [real,parameter=-11.7952894176]
numerical_parameters/loggyr2s [real,parameter=16.499103967]
numerical_parameters/loghecto [real,parameter=2.0]
numerical_parameters/logj2erg [real,parameter=7.0]
numerical_parameters/logk_boltzmann [real,parameter=-22.859916308]
numerical_parameters/logkilo [real,parameter=3.0]
numerical_parameters/logl_ab0 [real,parameter=13.637910954000002]
numerical_parameters/loglsun [real,parameter=26.5848963]
numerical_parameters/loglsun_bc [real,parameter=26.5868122]
numerical_parameters/logm2cm [real,parameter=2.0]
numerical_parameters/logmega [real,parameter=6.0]
numerical_parameters/logmpc2asat10pc [real,parameter=10.31442513287]
numerical_parameters/logpc2m [real,parameter=16.489350545]
numerical_parameters/logpi [real,parameter=0.497149873]
numerical_parameters/lograd2as [real,parameter=5.31442513287]
numerical_parameters/long_bn [integer,parameter=selected_int_kind(16)]
numerical_parameters/lsun [real,parameter=3.845e+26]
numerical_parameters/lsun40_bc [real,parameter=3.862e-07]
numerical_parameters/lsun_bc [real,parameter=3.862e+26]
numerical_parameters/lsun_erg [real,parameter=3.8450000000000004e+33]
numerical_parameters/lw_mode [integer]
numerical_parameters/m2cm [real,parameter=100.0]
numerical_parameters/m32cm3 [real,parameter=1000000.0]
numerical_parameters/m8crit [real,parameter=595167835572453.2]
numerical_parameters/m_atomic [real,parameter=1.66053873e-27]
numerical_parameters/m_atomic_g [real,parameter=1.66053873e-24]
numerical_parameters/m_be [real,parameter=1000.0]
numerical_parameters/m_cha_out [real]
numerical_parameters/m_electron [real,parameter=9.10938188e-31]
numerical_parameters/mean_mol [real,parameter=1.2281617489023304]
numerical_parameters/mega [real,parameter=1000000.0]
numerical_parameters/mend [real,parameter=2.0]

, Mcutoff !, MLongLive
numerical_parameters/milli [real,parameter=0.001]
numerical_parameters/mp_cgs [real,parameter=1.673e-24]
numerical_parameters/mpc2asat10pc [real,parameter=20626480624.64262]
numerical_parameters/mpc2cm [real,parameter=3.0856775807e+24]
numerical_parameters/mpc2m [real,parameter=3.0856775807e+22]
numerical_parameters/mpc_cgs [real,parameter=3.0857e+24]
numerical_parameters/mpckm2gyr [real,parameter=977.79222143002]
numerical_parameters/mres [real,parameter=500000.0]
numerical_parameters/msolar [real,parameter=1.9891e+30]
numerical_parameters/msolar_1030kg [real,parameter=1.9890999999999999]
numerical_parameters/msolar_g [real,parameter=1.9891e+33]
numerical_parameters/mstart [real,parameter=-2.0]
numerical_parameters/msun_cgs [real,parameter=1.989e+33]
numerical_parameters/msun_mpc3_cgs [real,parameter=6.769766377778569e-41]
numerical_parameters/mu_primordial [real,parameter=0.5847493469151946]
numerical_parameters/myr2s [real,parameter=31557600000000.0]
numerical_parameters/n_b_cgs [real,parameter=2.038744909036926e-07]
numerical_parameters/n_cloud_default [real,parameter=1000]

! dense, and diffuse density in the cold phase
numerical_parameters/n_cold_default [real,parameter=100.0]
numerical_parameters/n_h_cgs [real,parameter=1.5337477950684792e-07]
numerical_parameters/n_ionii [real,parameter=4000.0]
numerical_parameters/n_ioniii [real,parameter=90000.0]
numerical_parameters/n_spec [real,parameter=0.9667]
numerical_parameters/nextoutputid [integer]
numerical_parameters/nlev [integer]
numerical_parameters/nlev_max [integer,parameter=400]
numerical_parameters/nstarmassbinii [integer,parameter=64]
numerical_parameters/nstarmassbiniii [integer,parameter=64]
numerical_parameters/omega_b [real,parameter=0.0486]
numerical_parameters/omega_l [real,parameter=0.6911]
numerical_parameters/omega_m [real,parameter=0.3089]
numerical_parameters/pc2cm [real,parameter=3.0856775807e+18]
numerical_parameters/pc2m [real,parameter=3.0856775807e+16]
numerical_parameters/pi [real,parameter=3.1415926536]
numerical_parameters/pi4 [real,parameter=12.5663706144]
numerical_parameters/pio2 [real,parameter=1.5707963268]
numerical_parameters/pio4 [real,parameter=0.7853981634]
numerical_parameters/pisq [real,parameter=9.86960440115349]
numerical_parameters/popii_slope [real,parameter=2.3]
numerical_parameters/rad2as [real,parameter=206264.80624642622]
numerical_parameters/rad2degrees [real,parameter=57.29577951289617]
numerical_parameters/rdisk_half_scale [real,parameter=1.67834699]
numerical_parameters/rho_b_ast [real,parameter=6187863374.547873]
numerical_parameters/rho_b_cgs [real,parameter=4.189038942330162e-31]
numerical_parameters/rho_crit_cgs [real,parameter=8.619421692037372e-30]
numerical_parameters/rho_m_cgs [real,parameter=2.6625393606703443e-30]
numerical_parameters/rhocrit [real,parameter=277511434571.3454]
numerical_parameters/right_angle_degrees [real,parameter=90.0]
numerical_parameters/semi_circle_degrees [real,parameter=180.0]
numerical_parameters/sigma_8 [real,parameter=0.8159]
numerical_parameters/sigma_t [real,parameter=6.65e-25]
numerical_parameters/sigma_thomson [real,parameter=6.65245854e-29]
numerical_parameters/slope [real]
numerical_parameters/sqrt2 [real,parameter=1.4142135624]
numerical_parameters/sqrt2opi [real,parameter=0.7978845608]
numerical_parameters/sqrt2pi [real,parameter=2.5066282746]
numerical_parameters/sqrtatomic_mass_hydrogen [real,parameter=1.00396215]
numerical_parameters/sqrtdelta200 [real,parameter=14.142135623]
numerical_parameters/sqrtg [real,parameter=6.558435567945511e-05]
numerical_parameters/sqrtg_si [real,parameter=8.16859228998e-06]
numerical_parameters/sqrtk_boltzmann [real,parameter=3.715710295489e-12]
numerical_parameters/sqrtm_atomic [real,parameter=4.07497083425e-14]
numerical_parameters/sqrtmpc2m [real,parameter=175660968365.0]
numerical_parameters/sqrtmsolar [real,parameter=1410354565300000.0]
numerical_parameters/sqrtpi [real,parameter=1.7724538509]
numerical_parameters/sqrtrhocrit [real,parameter=526793.5407696816]
numerical_parameters/stdout_bn [integer,parameter=10]
numerical_parameters/surf_dens_norm_half [real,parameter=0.0297114]
numerical_parameters/t_crit [real,parameter=2200.0]
numerical_parameters/t_ion [real,parameter=10000.0]
numerical_parameters/t_univ [real,parameter=4.3549644e+17]
numerical_parameters/tree_mass [real]
numerical_parameters/v_com [real]
numerical_parameters/v_enrich_popiii [real,parameter=11000000.0]
numerical_parameters/v_out [real,parameter=11000000.0]
numerical_parameters/vbc [real]
numerical_parameters/x_hydrogen [real,parameter=0.778]
numerical_parameters/x_hydrogen_solar [real,parameter=0.7381]
numerical_parameters/xh [real,parameter=0.7523]
numerical_parameters/y_helium [real,parameter=0.222]
numerical_parameters/y_helium_solar [real,parameter=0.2485]
numerical_parameters/year_cgs [real,parameter=31560000.0]
numerical_parameters/yhe [real,parameter=0.2477]
numerical_parameters/z_crit [real,parameter=-5.0]
numerical_parameters/z_metals [real,parameter=5.36e-10]
numerical_parameters/z_metals_solar [real,parameter=0.0134]

resolution parameters

Quick access

Variables: m_ccsn_max, m_ccsn_min, m_ion, m_pisn_max, m_pisn_min, m_survive, zmax, zmin

Variables

resolution_parameters/m_ccsn_max [real,parameter=40]

Mass range for CCSNe
resolution_parameters/m_ccsn_min [real,parameter=10]
resolution_parameters/m_ion [real,parameter=5]

Stellar mass above which we account for ionizing radition
resolution_parameters/m_pisn_max [real,parameter=260]

Mass range for PISNe
resolution_parameters/m_pisn_min [real,parameter=140]
resolution_parameters/m_survive [real,parameter=0.81]

Stellar mass with lifetime 13.8Gyr.
resolution_parameters/zmax [real,parameter=35.0]
resolution_parameters/zmin [real,parameter=0.0]

Precomputed time-dependent quantities

crit_masses

Quick access

Variables: atomic_cooling_mass, mcrit
Routines: get_atomic_cooling_mass(), get_crit_mass(), get_m_vir(), get_mcrit_schauer(), get_mcrit_stacy(), get_t_vir(), set_mcrit()

Needed modules

numerical_parameters
cosmic_time

Variables

crit_masses/atomic_cooling_mass (nlev_max) [real,protected]
crit_masses/mcrit (nlev_max) [real,protected]

Subroutines and functions

subroutine crit_masses/set_mcrit()

Called from: asloth
Call to: get_crit_mass(), get_atomic_cooling_mass()

function crit_masses/get_crit_mass(z)

Determines critical mass for star formation at current redshift

Parameters: z [real,in] :: redshift
Return: get_crit_mass [real]
Called from: set_mcrit()
Call to: get_mcrit_schauer(), get_mcrit_stacy(), get_atomic_cooling_mass()

function crit_masses/get_mcrit_stacy(z)

Modified to also check Jeans Mass calculated with modified speed of sound See Stacy et al. 2011

Parameters: z [real,in] :: redshift
Return: get_mcrit_stacy [real]
Called from: get_crit_mass()
Call to: get_m_vir()

function crit_masses/get_mcrit_schauer(z)

based on Schauer+21, MNRAS, Volume 507, Issue 2, pp.1775-1787 Critical mass for halo collapse with LW feedback and baryonic streaming

Parameters: z [real,in] :: redshift
Return: get_mcrit_schauer [real]
Called from: get_crit_mass()

function crit_masses/get_atomic_cooling_mass(z)

the atomic cooling mass is the virial mass of 10^4 K

Parameters: z [real,in] :: redshift
Return: get_atomic_cooling_mass [real]
Called from: set_mcrit(), get_crit_mass()
Call to: get_m_vir()

function crit_masses/get_m_vir(z, t)

function determines the halo mass corresponding to a virial temperature we use the definition of Hummel et al 2012

Parameters

z [real,in] :: redshift
t [real,in] :: virial temperature

Return

get_m_vir [real]

Called from

get_mcrit_stacy(), get_atomic_cooling_mass()

function crit_masses/get_t_vir(m, z)

Parameters

m [real,in]
z [real,in]

Return

t [real]

Called from

sf_step()

cosmic_time

Quick access

Variables: a3_inv, alev, tlev, zlev
Routines: cosmic_dt(), distribute_cosmic_time(), dtdz(), read_scale_file(), set_cosmic_time()

Needed modules

numerical_parameters (nlev(), nlev_max())
resolution_parameters (zmin(), zmax())
input_files (scale_file()): List of input files

Variables

cosmic_time/a3_inv (nlev_max) [real,protected]

1/(a^3)
cosmic_time/alev (nlev_max) [real,protected]

cosmic expansion factor
cosmic_time/tlev (nlev_max) [real,protected]

time since big bang
cosmic_time/zlev (nlev_max) [real,protected]

redshift

Subroutines and functions

subroutine cosmic_time/set_cosmic_time()

subroutine to set redshift steps and compute expansion factors and cosmic time

Called from: asloth
Call to: read_scale_file(), distribute_cosmic_time(), cosmic_dt()

subroutine cosmic_time/distribute_cosmic_time()

subroutine to distribute redshift steps (either linearly or logarithmically)

Called from: set_cosmic_time()

subroutine cosmic_time/read_scale_file()

subroutine to read the redshift steps from scale file

Called from: set_cosmic_time()

function cosmic_time/dtdz(a)

Subroutine to calculate absolute value of dt/dz in seconds for density field normalized at reference epoch a0=1.

Notation:

Parameters

a [real,in] ::

expansion factor rel to a0=1

Uses

Return

res [real]

Use

numerical_parameters (hubble_cgs(), omega_l(), omega_m())

Called from

cosmic_dt()

function cosmic_time/cosmic_dt(z1, z2)

determines the time in seconds between redshift z1 and z2

Parameters

z1 [real,in]
z2 [real,in]

Return

res [real]

Called from

set_cosmic_time()

Call to

dtdz()

EPS wrapper

eps_wrapper

Description

module to interface with the Parkinson et al. 2008 tree generation routines

Quick access

Routines: make_eps_tree()

Needed modules

power_spectrum_parameters: Variables used to hold properties of the power spectrum
make_tree_module
tree_memory_arrays: This module is use for managing the memory of the merger tree
tree_memory_arrays_passable: This module is used to reference the merger tree
tree_routines: % A set of subroutines and functions for manipulating and utilizing the merger trees.
modified_merger_tree
defined_types (treenode())
numerical_parameters (mres())
resolution_parameters (zmin())
tree_initialization (set_tree_params()): prepares tree for running asloth

Subroutines and functions

subroutine eps_wrapper/make_eps_tree()

Called from: asloth
Call to: set_tree_params(), memory(), make_tree(), walk_tree()

Chemistry

chemistry

Quick access

Variables: abhe, x_ion
Routines: ch21(), ch22(), cl4(), cl64(), fh2_crit(), h2_cooling_rate(), h2_form_rate(), ph4(), t_hot2cold()

Needed modules

numerical_parameters

Variables

chemistry/abhe [real,parameter=y_helium/x_hydrogen/4.0]
chemistry/x_ion [real,parameter=0.0002]

residual ionization fraction

Subroutines and functions

function chemistry/t_hot2cold(n, t, z)

Parameters

n [real,in]
t [real,in]
z [real,in]

Return

res [real]

Called from

timescaledetermination()

Call to

fh2_crit(), h2_form_rate()

function chemistry/h2_form_rate(n, t, z)

Parameters

n [real,in]
t [real,in]
z [real,in]

Return

res [real]

Called from

t_hot2cold()

Call to

ch21(), ph4(), ch22()

function chemistry/fh2_crit(n, t, z)

Parameters

n [real,in]
t [real,in]
z [real,in]

Return

res [real]

Called from

t_hot2cold()

Call to

h2_cooling_rate()

function chemistry/h2_cooling_rate(n, t)

Parameters

n [real,in]
t [real,in]

Return

res [real]

Called from

fh2_crit()

Call to

cl4(), cl64()

function chemistry/ch22(temp)

Parameters: temp [real,in]
Return: res [real]
Called from: h2_form_rate()

function chemistry/ch21(temp)

Parameters: temp [real,in]
Return: res [real]
Called from: h2_form_rate()

function chemistry/ph4(z)

Parameters: z [real,in]
Return: res [real]
Called from: h2_form_rate()

function chemistry/cl64(temp)

Parameters: temp [real,in]
Return: res [real]
Called from: h2_cooling_rate()

function chemistry/cl4(temp)

Parameters: temp [real,in]
Return: res [real]
Called from: h2_cooling_rate()

Input files

Description

List of input files

Quick access

Variables: lines_lt_popii, lines_mmetals_popii, lines_qion_popii, mtree_file, output_string, pkinfile_dummy, popii_lt_file, popii_mmetals_file, popii_qion_file, popii_yields_file, popiii_yields_file, scale_file, tree_file, tree_name, tree_path

Variables

input_files/lines_lt_popii [integer]
input_files/lines_mmetals_popii [integer,parameter=27]
input_files/lines_qion_popii [integer]
input_files/mtree_file [character,parameter='tree_files/mass_estimates_lx13.dat']
input_files/output_string [character]
input_files/pkinfile_dummy [character,parameter='data/kvector_z1.txt']

is later passed on to pkinfile
input_files/popii_lt_file [character]
input_files/popii_mmetals_file [character,parameter='data/kobayashi2006_z1e-3_mmetal.txt']
input_files/popii_qion_file [character]
input_files/popii_yields_file [character,parameter='data/kobayashi2006_z1e-3.txt']
input_files/popiii_yields_file [character,parameter='data/yields_isotopes_sum_tr.dat']
input_files/scale_file [character]
input_files/tree_file [character]
input_files/tree_name [character]
input_files/tree_path [character]

Memory modules

Tree_memory_arrays

Description

This module is use for managing the memory of the merger tree

Quick access

Variables: mergertree_aux

Needed modules

defined_types

Variables

tree_memory_arrays/mergertree_aux (*) [treenode,target/allocatable]

Tree_memory_arrays_passable

Description

This module is used to reference the merger tree

Quick access

Variables: mergertree, number_of_nodes
Routines: tree_index()

Needed modules

defined_types

Variables

tree_memory_arrays_passable/mergertree (*) [treenode,pointer]
tree_memory_arrays_passable/number_of_nodes [integer]

Subroutines and functions

function tree_memory_arrays_passable/tree_index(node)

Parameters: node [treenode,pointer]
Return: tree_index [integer,pure]
Called from: associate_siblings()

Star formation

SF_routines

Quick access

Routines: add_hmstars(), add_to_base_mstariisurv(), coldgasbindingenergy_cold(), coldgasbindingenergy_hot(), coldgasbindingenergy_nfw(), coldgasbindingenergy_stellar(), dm_concen(), get_n_ion(), hms_feedback(), hotgasbindingenergy_disk(), hotgasbindingenergy_hot(), hotgasbindingenergy_nfw(), imfsampling(), nfw_const(), rm_hmstars(), snedrivenoutflow(), survivingstarlist(), timescaledetermination(), writellstarstofileanddel()

Needed modules

defined_types
numerical_parameters
resolution_parameters
random_object: This is an object oriented version of the ran3 algorithm from numerical recipes Before using a rng type object it needs to be initialized by calling rng%init(seed) where seed is an integer seed Afterwards rng%get_ran() will return a random real in the range [0,1)
utility: General purpose utility functions
metal_functions: Helper functions for handling metals and computing abundances and metallicities
trace_mdf
metals: main module for handling metals
populations: This module contains the type for storing IMFs, and the properties of the stellar populations
chemistry
cosmic_time (zlev(), tlev())

Subroutines and functions

subroutine sf_routines/add_hmstars(p_node, ii, timeborn, num_, zgas, pop)

Parameters

p_node [treenode,inout]
ii [integer,in]
timeborn [real,in]
num [integer,in]
zgas (n_elements) [real,in]
pop [integer,in]

Called from

imfsampling()

subroutine sf_routines/rm_hmstars(p_node, highstar)

Parameters

p_node [treenode,inout]
highstar [stars_highmass,inout,pointer]

Called from

hms_feedback()

function sf_routines/get_n_ion(p_node, t_current, del_t)

This routine calculates the total number of ionizing photons emitted during this time-step

Parameters

p_node [treenode,inout] :: the halo
t_current [real,in] :: beginning of the sub-step
del_t [real,in] :: time-step

Return

n_ion_tot [real] :: number of total ionizing photons

Called from

sf_step()

subroutine sf_routines/hms_feedback(p_node, t_current, totalmetalejectathisstep, totalsneenergythisstep, m_heat, rel_del_t)

This routine calculates the cold gas mass that will be heated/ blown out from the halo and the explosion energy/ ejecta from SNe.

Parameters

p_node [treenode,inout] :: the halo
t_current [real,in] :: beginnng of the sub-step
totalmetalejectathisstep (n_elements) [real,inout] :: metals produced in this time-step
totalsneenergythisstep [real,out] :: Msun cm^2 s^-2
m_heat [real,out] :: mass that will trasfer from cold to hot in this time-step
rel_del_t [real,in] :: time-step

Called from

sf_step()

Call to

add_to_m_metals(), rm_hmstars()

subroutine sf_routines/add_to_base_mstariisurv(this_node)

Inherit M_star_II_surv to the base node at the final redshift

Parameters: this_node [treenode,inout]
Called from: asloth

subroutine sf_routines/survivingstarlist(p_node, num, ii, z, timeborn, pop)

Non-standard routine if users are interested in stellar information of survival stars in satellites

Parameters

p_node [treenode,inout] :: the halo
num [integer,in] :: number of stars in this IMF mass bin
ii [integer,in] :: index of the IMF mass bin
z (n_elements) [real,in] :: metallicity of the star
timeborn [real,in] :: time of star formation
pop [integer,in] :: stellar population

Called from

imfsampling()

subroutine sf_routines/writellstarstofileanddel(p_node)

Parameters: p_node [treenode,inout]
Called from: sf_step()

subroutine sf_routines/imfsampling(p_node, totalmassnewstars, m_star_form, t_current, this_rng, zgas, pop)

This routine samples individual stars from the IMF and store massive stars in the list.

Parameters

p_node [treenode,inout] :: the halo
totalmassnewstars [real,inout] :: total mass of newly formed stars
m_star_form [real,in] :: estimated total stellar mass based on cold gas mass and star formation efficiency
t_current [real,in] :: the time in the beginning of the time-step
this_rng [rng,inout] :: random number generator
zgas (n_elements) [real,in] :: gas metallicity
pop [population,in] :: stellar population

Use

trace_mdf

Called from

sf_step()

Call to

get_mdf_bin(), poisson(), add_hmstars(), survivingstarlist(), add_to_node_mdf()

subroutine sf_routines/dm_concen(m_halo, z, dm_concentration)

This routine determins the concentration of the NFW profile. We adopt the fitting curve by Correa et al. 2015 (doi:10.1093/mnras/stv1363) Planck cosmology (appendix B) Note that the fitting functions are valid for z=0-10 based on private conversation

Parameters

m_halo [real,in] :: virial mass of the halo
z [real,in] :: current redshift
dm_concentration [real,out] :: (arbitrary) constant value, reference from Fig. 7 in Correa et al. 2015 for haloes > 10^5 Msun

Called from

sf_step()

subroutine sf_routines/timescaledetermination(p_node, v_dyn, t_dyn, ncold, n_hot, coldgas_tff, r_vir_current, dm_concentration, t_vir, t_cool)

This routines determines all the relative timescales that we need. Note that the time scales we eventually use are dimensionless.

Parameters

p_node [treenode,inout] :: This also includes “dead” stars
v_dyn [real,inout] :: s; dynamical time of the halo center
t_dyn [real,inout] :: dynamical timescale of central part of the halo
ncold [real,inout] :: number density of cold gas
n_hot [real,inout] :: number density of hot gas
coldgas_tff [real,inout] :: free fall time of cold gas
r_vir_current [real,in] :: the current virial raius of the halo
dm_concentration [real,in] :: dark matter halo concentration
t_vir [real,in] :: virial temperature of the halo
t_cool [real,inout] :: cooling time of hot gas

Called from

sf_step()

Call to

t_hot2cold()

function sf_routines/nfw_const(r_s, r_vir_current)

Parameters

r_s [real,in]
r_vir_current [real,in]

Return

a [real]

Called from

hotgasbindingenergy_nfw(), coldgasbindingenergy_nfw()

function sf_routines/hotgasbindingenergy_nfw(r_s, r_vir_current, m_hot, m_peak)

Parameters

r_s [real,in]
r_vir_current [real,in]
m_hot [real,in]
m_peak [real,in]

Return

be_hot_nfw [real]

Called from

snedrivenoutflow()

Call to

nfw_const()

function sf_routines/hotgasbindingenergy_disk(r_s, r_vir_current, m_hot, m_disk)

Parameters

r_s [real,in]
r_vir_current [real,in]
m_hot [real,in]
m_disk [real,in]

Return

be_hot_disk [real]

Called from

snedrivenoutflow()

function sf_routines/hotgasbindingenergy_hot(r_vir_current, m_hot)

Parameters

r_vir_current [real,in]
m_hot [real,in]

Return

be_hot_hot [real]

Called from

snedrivenoutflow()

function sf_routines/coldgasbindingenergy_nfw(r_s, r_vir_current, m_cold, m_peak)

Parameters

r_s [real,in]
r_vir_current [real,in]
m_cold [real,in]
m_peak [real,in]

Return

be_cold_nfw [real]

Called from

snedrivenoutflow()

Call to

nfw_const()

function sf_routines/coldgasbindingenergy_stellar(r_s, m_cold, m_stellar)

Parameters

r_s [real,in]
m_cold [real,in]
m_stellar [real,in]

Return

be_cold_stellar [real]

Called from

snedrivenoutflow()

function sf_routines/coldgasbindingenergy_hot(r_s, r_vir_current, m_cold, m_hot)

Parameters

r_s [real,in]
r_vir_current [real,in]
m_cold [real,in]
m_hot [real,in]

Return

be_cold_hot [real]

Called from

snedrivenoutflow()

function sf_routines/coldgasbindingenergy_cold(r_s, m_cold)

Parameters

r_s [real,in]
m_cold [real,in]

Return

be_cold_cold [real]

Called from

snedrivenoutflow()

subroutine sf_routines/snedrivenoutflow(p_node, r_vir_current, dm_concentration, totalsneenergythisstep, del_m_hot, totalmassnewstars, m_out_hot, m_out_cold)

This routines determines the binding energy of hot gas and cold gas. It then compare the SNe energy and the binding energy to determine how much gas SNe will blow out of the halo.

Parameters

p_node [treenode,inout] :: the halo
r_vir_current [real,in] :: current virial radius of the halo
dm_concentration [real,in] :: dark matter halo’s concentration
totalsneenergythisstep [real,inout] :: sum of SNe energy that occur in this time-step
del_m_hot [real,in] :: smoothly accreted hot gas in this time-step
totalmassnewstars [real,in] :: total newly formed stellar mass in this time-step
m_out_hot [real,inout] :: hot gas mass that ends up in outflow in this time-step
m_out_cold [real,inout] :: cold gas mass that ends up in outflow in this time-step

Called from

sf_step()

Call to

hotgasbindingenergy_nfw(), hotgasbindingenergy_disk(), hotgasbindingenergy_hot(), coldgasbindingenergy_nfw(), coldgasbindingenergy_stellar(), coldgasbindingenergy_hot(), coldgasbindingenergy_cold()

Star_formation

Quick access

Routines: add_parent_to_tree(), dt_adaptive(), m_star_dot(), prepare_node(), sf_step()

Needed modules

bubble_tree: This module containes routines for adding feedback of haloes to the tracing system and for testing whether a halo or a specific point in space is affected by the feedback
defined_types
numerical_parameters
resolution_parameters
metal_functions: Helper functions for handling metals and computing abundances and metallicities
snowplow
random_object: This is an object oriented version of the ran3 algorithm from numerical recipes Before using a rng type object it needs to be initialized by calling rng%init(seed) where seed is an integer seed Afterwards rng%get_ran() will return a random real in the range [0,1)
sf_routines
input_files: List of input files
crit_masses (get_t_vir())
utility (iunit_list()): General purpose utility functions
virial_radius (rvir_mpc())
igm_z: utility routine for inheriting the IGM metallicity accross time
metalmix_dz

Subroutines and functions

subroutine star_formation/prepare_node(p_node, m_star_max, is_ion)

Parameters

p_node [treenode,inout] :: parent node
m_star_max [real,out]
is_ion [logical,out] :: set from most massive child

Called from

sf_step()

Call to

check_m_metals(), inherit_igm_z()

subroutine star_formation/sf_step(node)

Parameters: node [treenode,inout,target]
Called from: asloth
Call to: prepare_node(), rvir_mpc(), dm_concen(), get_t_vir(), check_m_metals(), get_metallicity(), calc_dz(), metalpoor(), timescaledetermination(), dt_adaptive(), m_star_dot(), imfsampling(), get_n_ion(), hms_feedback(), snedrivenoutflow(), assert(), v_plow_ad(), r_ion_ad(), writellstarstofileanddel()

subroutine star_formation/add_parent_to_tree(node)

Parameters: node [treenode,inout]
Called from: asloth
Call to: add_bubble_to_tree(), get_m_metals()

function star_formation/dt_adaptive(p_node, coldgas_tff, t_dyn, del_m_hot, del_t, t_step, pop_now)

timestep in sec

Parameters

p_node [treenode,in]
coldgas_tff [real,in]
t_dyn [real,in]
del_m_hot [real,in]
del_t [real,in]
t_step [real,in]
pop_now [integer,in]

Return

dt_adaptive [real]

Called from

sf_step()

Call to

m_star_dot()

function star_formation/m_star_dot(m_cold, coldgas_tff, pop_now)

star formation rate in this substep (Msun/s)

Parameters

m_cold [real,in]
coldgas_tff [real,in]
pop_now [integer,in]

Return

m_star_dot [real]

Use

numerical_parameters (etaii(), etaiii())

Called from

sf_step(), dt_adaptive()

Stellar feedback

feedback_routines

Quick access

Routines: node_feedback(), self_enrichment(), statistical_feedback()

Needed modules

bubble_tree: This module containes routines for adding feedback of haloes to the tracing system and for testing whether a halo or a specific point in space is affected by the feedback
defined_types
feedback_arrays (v_enriched(), v_ionized())
crit_masses

Subroutines and functions

subroutine feedback_routines/node_feedback(this_node)

Parameters: this_node [treenode,target]
Called from: asloth
Call to: self_enrichment(), statistical_feedback(), bubble_check(), ion_check()

subroutine feedback_routines/self_enrichment(node)

This routine computes feedback exactly as the bubble-tree in the case that there is only self-enrichment

Parameters: node [treenode]
Called from: node_feedback()

subroutine feedback_routines/statistical_feedback(this_node)

Parameters: this_node [treenode,target]
Use: random_object
Called from: node_feedback()
Call to: get_ran()

snowplow

Quick access

Routines: r_ion_ad(), v_plow_ad()

Needed modules

Subroutines and functions

function snowplow/v_plow_ad(m, z, r, r_v)

Parameters

m [real,in]
z [real,in]
r [real,in]
r_v [real,in]

Return

v [real]

Called from

sf_step()

function snowplow/r_ion_ad(nion, r_old, z, del_t, mhalo)

Parameters

nion [real,in]
r_old [real,in]
z [real,in]
del_t [real,in]
mhalo [real,in]

Return

r_new [real]

Called from

sf_step()

Call to

rvir_mpc(), rvir()

Feedback tracing

feedback_arrays

Quick access

Variables: sfr, v_enriched, v_ionized
Routines: add_to_feedback_volumes(), add_to_sfr()

Needed modules

numerical_parameters (nlev_max(), year_cgs(), pi())
defined_types
cosmic_time (tlev())

Variables

feedback_arrays/sfr (2,nlev_max) [real,protected/optional/default=0]

SFR: population, timestep
feedback_arrays/v_enriched (nlev_max) [real,protected/optional/default=0]
feedback_arrays/v_ionized (nlev_max) [real,protected/optional/default=0]

(in physical Mpc^3)

Subroutines and functions

subroutine feedback_arrays/add_to_feedback_volumes(node)

Parameters: node [treenode]
Called from: asloth

subroutine feedback_arrays/add_to_sfr(node)

Parameters: node [treenode]
Called from: asloth

volume_fractions

Quick access

Routines: get_fracs()

Needed modules

cosmic_time (alev(), zlev())
bubble_tree: This module containes routines for adding feedback of haloes to the tracing system and for testing whether a halo or a specific point in space is affected by the feedback
input_files: List of input files
numerical_parameters
feedback_arrays (v_ionized(), v_enriched())
config (cubic_box())
utility (iunit_list()): General purpose utility functions

Subroutines and functions

subroutine volume_fractions/get_fracs(jlevel)

Parameters: jlevel [integer,in]
Use: random_object
Called from: asloth
Call to: get_ran(), position_check()

Outputs

to_file

Quick access

Types: file_specs
Routines: add_timestamp(), close_outputs(), init_outputs(), outputs_satellites(), write_file(), write_files(), write_files_reali(), write_mw_properties()

Needed modules

resolution_parameters
numerical_parameters
input_files: List of input files
utility: General purpose utility functions
converters: Module for converting numerical values to strings
config (config_file())
cosmic_time
defined_types
feedback_arrays
virial_radius (rvir_mpc())

Types

type to_file/file_specs
Type fields
% file_string [character]

% head1 [character]

% head2 [character]

% out_dir [character]

% stuff (30,nlev_max) [real]

% var_name [character]

Variables

Subroutines and functions

subroutine to_file/init_outputs()

Called from: asloth
Call to: add_timestamp()

subroutine to_file/write_file(fs, var_name, ncoll)

Parameters

fs [file_specs]
var_name [character]
ncoll [integer] :: loop writes one line

Called from

write_files()

subroutine to_file/outputs_satellites(count)

Parameters: count [integer]
Use: tree_memory_arrays_passable, trace_mdf
Called from: asloth
Call to: rvir_mpc(), add_to_node_mdf()

subroutine to_file/write_files_reali()

Use: tree_memory_arrays_passable, input_files, metal_functions, trace_mdf
Called from: asloth
Call to: rvir_mpc(), add_to_node_mdf()

subroutine to_file/write_files(output)

Parameters: output [file_specs,inout]
Called from: asloth
Call to: real_to_string(), write_file()

subroutine to_file/close_outputs()

go through list of possible open file IDs

Called from: asloth

subroutine to_file/add_timestamp(output_path)

Parameters: output_path [character,inout]
Called from: init_outputs()
Call to: int_to_string4(), int_to_string2()

subroutine to_file/write_mw_properties()

Use: tree_memory_arrays_passable, input_files
Called from: asloth

Utility modules

Converters

Description

Module for converting numerical values to strings

Quick access

Routines: bool2int(), int_to_string2(), int_to_string3(), int_to_string4(), real_to_string(), real_to_string3()

Subroutines and functions

function converters/real_to_string(numb)

Parameters: numb [real,in]
Return: string [real]
Called from: write_files()

function converters/real_to_string3(numb)

Parameters: numb [real,in]
Return: string [real]
Called from: asloth

function converters/int_to_string2(numb)

Parameters: numb [integer,in]
Return: string [real]
Called from: add_timestamp()

function converters/int_to_string3(numb)

Parameters: numb [integer,in]
Return: string [real]

function converters/int_to_string4(numb)

Parameters: numb [integer,in]
Return: string [real]
Called from: add_timestamp()

function converters/bool2int(boo)

Parameters: boo [logical,in]
Return: int_out [integer]

Utility

Description

General purpose utility functions

Quick access

Variables: current_cpu_time, getpid, iunit_list, start_cpu_time
Routines: assert(), i_realloc_1d(), mem_report(), print_bar(), r_realloc_1d(), r_realloc_2d()

Needed modules

input_files: List of input files
numerical_parameters (stdout())
ifport

Variables

utility/current_cpu_time [real]
utility/getpid [intrinsic]
utility/iunit_list (16) [integer]

list of file IDs
utility/start_cpu_time [real]

Subroutines and functions

subroutine utility/mem_report()

Called from: init_tree(), read_tree(), asloth

subroutine utility/assert(test_passed, message)

Parameters

test_passed [logical,in]
message [character,in]

Called from

metalpoor(), get_element_info(), sf_step(), kroupa_p_func(), kroupa_m_func(), massprobabilityimf(), compute_savgol_coeff(), savgol_filter(), fact()

subroutine utility/r_realloc_1d(a, n_new)

Parameters

a (*) [real,inout,allocatable]
n_new [integer,in]

subroutine utility/i_realloc_1d(a, n_new)

Parameters

a (*) [integer,inout,allocatable]
n_new [integer,in]

subroutine utility/r_realloc_2d(a, n1_new, n2_new)

Parameters

a (,) [real,inout,allocatable]
n1_new [integer,in]
n2_new [integer,in]

subroutine utility/print_bar(prog, msg[, complete])

Parameters

prog [real]
msg [character,in]
complete [logical]

Called from

init_tree(), filter_virial_masses(), read_tree(), asloth

Virial radius

Quick access

Routines: rvir(), rvir_mpc()

Needed modules

numerical_parameters

Subroutines and functions

function virial_radius/rvir(mhalo, z)

Parameters

mhalo [real,in]
z [real,in]

Return

r [real]

Called from

r_ion_ad(), rvir_mpc()

function virial_radius/rvir_mpc(mhalo, z)

Parameters

mhalo [real,in] :: halo mass
z [real,in] :: redshift

Return

r [real]

Called from

r_ion_ad(), sf_step(), outputs_satellites(), write_files_reali()

Call to

rvir()

Check flags

Description

Module to check whether the flags defined and undefined in asloth.h are an ok combination

Quick access

Routines: check_compiler_flags()

Subroutines and functions

subroutine check_flags/check_compiler_flags()

Called from: asloth

Random number generator

Description

This is an object oriented version of the ran3 algorithm from numerical recipes Before using a rng type object it needs to be initialized by calling rng%init(seed) where seed is an integer seed Afterwards rng%get_ran() will return a random real in the range [0,1)

Quick access

Types: rng
Variables: rng_seed
Routines: get_ran(), init(), poisson()

Types

type random_object/rng
Type fields
% iff [integer]

% inext [integer]

% inextp [integer]

% ma (55) [integer]

% mj [integer]

Variables

random_object/rng_seed [integer]

Subroutines and functions

subroutine random_object/init(this, idum)

initializes the RNG

Parameters

this [real]
idum [integer,in]

function random_object/get_ran(this)

Retuns uniformly distributed random numbers in the range [0,1)

Parameters: this [real]
Return: get_ran [real]
Called from: split(), statistical_feedback(), dz_external(), dz_internal(), poisson(), get_fracs()

function random_object/poisson(this, mu)

Returns poisson distributed random numbers. Works only for small numbers (mu < 10)

Parameters

this [real]
mu [real,in] :: parameter of the poisson distribution

Return

poisson [integer]

Called from

imfsampling()

Call to

get_ran()

Metal modules

Modules and functions for the handling of metals

Metals

Description

main module for handling metals

Quick access

Types: element, element_list
Variables: index_carbon, index_iron, n_elements, tracked_elements
Routines: append(), get_element_index(), get_element_info(), init_element()

Needed modules

utility (assert()): General purpose utility functions

Types

type metals/element
container type for individual elements
Type fields
% atomic_mass [real]

% element_name [character]

% solar_abund [real]
type metals/element_list
this list is a container for all
Type fields
% list (*) [element,allocatable]

% n [integer,optional/default=0]

Variables

metals/index_carbon [integer,optional/default=0]
metals/index_iron [integer,optional/default=0]

index of carbon and iron in yields and metal arrays
metals/n_elements [integer]
metals/tracked_elements [element_list]

Subroutines and functions

subroutine metals/append(this, el)

appends an element to an element list

Parameters

this [real] :: the list of elements
el [element] :: the element to be appended

function metals/get_element_index(this, el_name)

returns the the index at which an element is found in the yield table

Parameters

this [real] :: list of tracked elements
el_name [character,in] :: name of the element to look up

Return

i [integer]

Called from

readelementid()

function metals/init_element(el_name)

Parameters: el_name [character,in]
Return: this [element]
Call to: get_element_info()

subroutine metals/get_element_info(el_name, atomic_mass, solar_abund)

Parameters

el_name [character,in] :: which element are we looking up?
atomic_mass [real,out] :: mean atomic weight
solar_abund [real,out] :: converting from the log(X/H)+12 scale

Called from

init_element()

Call to

assert()

Read Yields

Description

module for reading yields from the appropirate files

Quick access

Routines: assign_yields(), assign_yields_general(), get_elem_num(), readelementid()

Needed modules

populations: This module contains the type for storing IMFs, and the properties of the stellar populations
config (config_file())
metals: main module for handling metals

Subroutines and functions

subroutine read_yields/assign_yields()

Called from: asloth
Call to: readelementid(), assign_yields_general()

subroutine read_yields/readelementid()

Called from: assign_yields()
Call to: get_element_index()

subroutine read_yields/assign_yields_general(fileyields, pop)

Parameters

fileyields [character,in]
pop [population,in] :: go through IMF bins

Called from

assign_yields()

Call to

get_elem_num()

subroutine read_yields/get_elem_num(f, num_ele, mass_num)

defines the number of elements and the number of different stellar masses in the yields file hard-coded file names. If you want to use a different table, add it here.

Parameters

f [character,in] :: file name
num_ele [integer,out] :: number of elements in the file
mass_num [integer,out] :: number of masses in the table

Called from

assign_yields_general()

Metal functions

Description

Helper functions for handling metals and computing abundances and metallicities

Quick access

Routines: add_to_m_metals(), cfe(), check_m_metals(), coh(), feh(), get_m_metals(), get_melement(), get_metallicity(), metalpoor(), zcal()

Needed modules

numerical_parameters
defined_types (treenode())
utility (assert()): General purpose utility functions
metals: main module for handling metals

Subroutines and functions

function metal_functions/get_metallicity(m_h, mmetals, ii)

gas metallicity of this halo This function assumed homogeneous mixing of metals with gas Because this homogeneous metallicity is needed as input to calculate dZ in order to corret to an inhomogeneous metallicity

Parameters

m_h [real,in]
mmetals [real,in] :: mass hydrogen, mass metals
ii [integer,in] :: index in abundance array

Return

get_metallicity [real]

Called from

sf_step()

subroutine metal_functions/check_m_metals(thishalo)

if not yet allocated, allocate ThisHalo%m_metals and set to zero

Parameters: thishalo [treenode]
Called from: add_to_m_metals(), prepare_node(), sf_step()

subroutine metal_functions/add_to_m_metals(thishalo, mmetal, iadd)

add metals to %m_metals node property :p integer iadd [in]: is index of which metal type should be added

Parameters

thishalo [treenode]
mmetal (n_elements) [real,in]

Called from

hms_feedback()

Call to

check_m_metals()

function metal_functions/get_m_metals(thishalo, iget)

get metal mass array from %m_metals node property :p integer iget [in]: is index of which metal type is wanted

Parameters: thishalo [treenode]
Return: get_m_metals (n_elements) [real] :: function returns an array
Called from: get_m_metals(), add_parent_to_tree()
Call to: get_m_metals()

function metal_functions/get_melement(thishalo, index_metal)

get mass of this halo of one specific element

Parameters

thishalo [treenode]
index_metal [integer,in]

Return

get_melement [real]

Called from

feh(), coh(), cfe()

function metal_functions/feh(thishalo, dz_now)

gas metallicity of this halo [Fe/H]

Parameters

thishalo [treenode]
dz_now [real,in]

Return

feh [real]

Called from

metalpoor()

Call to

get_melement()

function metal_functions/coh(thishalo, dz_now)

gas metallicity of this halo [C/H]

Parameters

thishalo [treenode]
dz_now [real,in]

Return

coh [real]

Called from

metalpoor()

Call to

get_melement()

function metal_functions/cfe(thishalo)

gas metallicity of this halo [C/Fe]

Parameters: thishalo [treenode]
Return: cfe [real]
Call to: get_melement()

function metal_functions/metalpoor(thishalo, z_in, dz_now)

Parameters

thishalo [treenode]
z_in [real,in]
dz_now [real,in]

Return

metalpoor [logical]

Called from

sf_step()

Call to

feh(), assert(), coh()

subroutine metal_functions/zcal(metallicity, gas, metals)

20200820 Li-Hsin This routine also calculates the metallciity but in linear scale. This is simply for the convenience when we want to keep information from the last time step.

Parameters

metallicity [real,out]
gas [real,in]
metals [real,in]

IGM Metallicity routines

Description

utility routine for inheriting the IGM metallicity accross time

Quick access

Routines: inherit_igm_z()

Needed modules

defined_types

Subroutines and functions

subroutine igm_z/inherit_igm_z(p_node)

Parameters: p_node [treenode,inout] :: parent node
Called from: prepare_node()

Metal Mix dZ

Quick access

Variables: dztable_12, dztable_23, dztable_34, dztable_45, dztable_58
Routines: calc_dz(), dz_external(), dz_internal(), read_lookups()

Needed modules

numerical_parameters
defined_types
random_object: This is an object oriented version of the ran3 algorithm from numerical recipes Before using a rng type object it needs to be initialized by calling rng%init(seed) where seed is an integer seed Afterwards rng%get_ran() will return a random real in the range [0,1)

Variables

metalmix_dz/dztable_12 (1000) [real,protected]
metalmix_dz/dztable_23 (1000) [real,protected]
metalmix_dz/dztable_34 (1000) [real,protected]
metalmix_dz/dztable_45 (1000) [real,protected]
metalmix_dz/dztable_58 (1000) [real,protected]

Subroutines and functions

subroutine metalmix_dz/read_lookups()

Called from: init_tree()

function metalmix_dz/dz_external(rng_obj, z)

dilution from Yuta Tarumi: ApJ, Volume 897, Issue 1, id.58 based on Renaissance Simulation dZ = (Mmetals/Mgas)_densestGas / (Mmetals/Mgas)_halo hydrogen mass in this halo / dZ = hydrogen mass with which metals mix in densest region

Parameters

rng_obj [rng,inout]
z [real,in]

Return

dz_external [real]

Called from

calc_dz()

Call to

get_ran()

function metalmix_dz/dz_internal(rng_obj)

Parameters: rng_obj [rng,inout]
Return: dz_internal [real] :: to be consistent with previous use of dZ
Called from: calc_dz()
Call to: get_ran()

function metalmix_dz/calc_dz(m_metals_int, rng_obj, z_now)

metal mixing correction based on Tarumi+20, ApJ, Volume 897, Issue 1, id.58 dZ: log-differenze between metallicity of all gas in a halo and the dense gas

Parameters

m_metals_int [real,in]
rng_obj [rng,inout]
z_now [real,in]

Return

calc_dz [real]

Called from

sf_step()

Call to

dz_external(), dz_internal()

Metallicity distribution function

Quick access

Variables: mdf_zarray, mdfdz, mdfnbin, mdfnpad, mdfzmax, mdfzmin
Routines: add_to_base_mdf(), add_to_node_mdf(), get_mdf_bin(), init_mdf()

Needed modules

defined_types

Variables

trace_mdf/mdf_zarray (*) [real,allocatable/protected]
trace_mdf/mdfdz [real,parameter=0.1]

MDF range, bin width
trace_mdf/mdfnbin [integer]
trace_mdf/mdfnpad [integer,parameter=2]
trace_mdf/mdfzmax [real,parameter=1]
trace_mdf/mdfzmin [real,parameter=-9]

Subroutines and functions

subroutine trace_mdf/init_mdf()

Called from: init_tree()
Call to: get_mdf_bin()

function trace_mdf/get_mdf_bin(zgas)

Parameters: zgas [real]
Return: get_mdf_bin [integer] :: [Fe/H]
Called from: imfsampling(), init_mdf()

subroutine trace_mdf/add_to_node_mdf(this_node, mdf_nstar, mdf_index, idx2)

add MDF_Nstar stars to the MDF of This_Node

Parameters

this_node [treenode,inout]
mdf_nstar [real,in]
mdf_index [integer,in]
idx2 [integer,in]

Called from

imfsampling(), outputs_satellites(), write_files_reali()

subroutine trace_mdf/add_to_base_mdf(this_node)

Inherit MDF to the base node at the final redshift

Parameters: this_node [treenode,inout]
Called from: asloth

Spatially resolved feedback

Modules and routines for efficiently tracing spatially resolved feedback

Bubble Tree

Description

This module containes routines for adding feedback of haloes to the tracing system and for testing whether a halo or a specific point in space is affected by the feedback

Quick access

Needed modules

numerical_parameters
defined_types
config (cubic_box())
cosmic_time (alev(), a3_inv())
crit_masses (atomic_cooling_mass())
metal_functions: Helper functions for handling metals and computing abundances and metallicities
bubble_tree_types

Variables

bubble_tree/head_nodes (nlev_max) [n_pointer]
bubble_tree/head_nodes_ion (nlev_max) [n_pointer]
bubble_tree/l_box_max [real]
bubble_tree/mass_diff [real]
bubble_tree/max_level [integer,parameter=20]

Subroutines and functions

subroutine bubble_tree/start_btree()

initializes tree and sets up the head nodes

Called from: asloth

subroutine bubble_tree/add_bubble_to_tree(nodeadd, mmetal_add)

Adds ionized an enriched bubble of a node to the tree

Parameters

nodeadd [treenode,inout]
mmetal_add (n_elements) [real,in] :: metal mass in this SN shell

Called from

add_parent_to_tree()

Call to

find_bnode(), increase_size(), copy_props_to_bubble()

subroutine bubble_tree/find_bnode(jlevel, x, bnode, r[, ion_tree])

Parameters

jlevel [integer,in]
x (3) [real,in]
bnode [bubble_node,out,pointer] :: associate parent node
r [real,in]
ion_tree [logical,in,]

Called from

add_bubble_to_tree()

Call to

grid_check()

subroutine bubble_tree/rm_bubble(node, to_remove)

Parameters

node [bubble_node,pointer]
to_remove [integer]

Call to

mv_bubble()

subroutine bubble_tree/mv_bubble(n_from, i_from, n_to, i_to)

moves the bubble to_move to the node move_to and fixes all the pointers afterwards

Parameters

n_from [bubble_node,inout,pointer]
i_from [integer,in]
n_to [bubble_node,inout,pointer]
i_to [integer,in]

Called from

rm_bubble()

subroutine bubble_tree/copy_props_to_bubble(bnode, bubble, nodeadd, mmetal_add)

Parameters

bnode [bubble_node,pointer]
bubble [integer,in] :: Index of Bubble
nodeadd [treenode,inout]
mmetal_add (*) [real,in] :: metal mass in this SN shell

Called from

add_bubble_to_tree()

subroutine bubble_tree/bubble_check(this_node)

routine to check whether This_Node is externally enricher and/or ionized

Parameters: this_node [treenode,inout,target]
Called from: node_feedback()
Call to: dist_check_allow_same(), grid_check()

subroutine bubble_tree/ion_check(this_node)

routine to check whether This_Node is ionized the treatment of haloes above the atomic cooling limit is based on Visbal et al. 2016 (MNRAS 460, L59-L63)

Parameters: this_node [treenode,inout,target]
Called from: node_feedback()
Call to: dist_check(), grid_check()

subroutine bubble_tree/clean_tree(jlevel)

Parameters: jlevel [integer,in]
Called from: asloth
Call to: clean_bnode()

subroutine bubble_tree/clean_bnode(jlevel, bnode, n_merge)

Parameters

jlevel [integer,in]
bnode [bubble_node,inout,pointer]
n_merge [integer,inout]

Called from

clean_tree()

subroutine bubble_tree/merge_into_bubble(jlevel, x_in, v_in, l_in, x_out, v_ion_current, l_out, bnode, bubble_in)

Parameters

jlevel [integer,in]
x_in (3) [real,in]
v_in [real,in]
l_in [real,in]
x_out (3) [real,out]
v_ion_current [real,out]
l_out [real,out]
bnode [bubble_node,pointer]
bubble_in [integer,in]

subroutine bubble_tree/position_check(x, j_now, is_ion, is_enr)

routine to check whether This_Node is externally enricher and/or ionized

Parameters

x (3) [real,in]
j_now [integer,in]
is_ion [logical,out]
is_enr [logical,out]

Called from

get_fracs()

Call to

grid_check()

function bubble_tree/count_ion(x, j_now, ion_tree)

routine to check whether This_Node is externally enricher and/or ionized

Parameters

x (3) [real,in]
j_now [integer,in]
ion_tree [logical,in]

Return

n_ion [integer]

Call to

grid_check()

subroutine bubble_tree/grid_check(ijk, bnode, x_in, caller)

Parameters

ijk (3) [integer,in]
bnode [bubble_node,pointer]
x_in (3) [real,in]
caller [character,in]

Called from

find_bnode(), bubble_check(), ion_check(), position_check(), count_ion()

function bubble_tree/dist_check_allow_same(x_ref, x, r)

Parameters

x_ref (3) [real,in]
x (3) [real,in]
r [real,in]

Return

dist_check_allow_same [logical,pure]

Called from

bubble_check()

function bubble_tree/dist_check(x_ref, x, r)

Parameters

x_ref (3) [real,in]
x (3) [real,in]
r [real,in]

Return

dist_check [logical,pure]

Called from

ion_check()

Bubble tree types

Quick access

Types: bubble_node, n_pointer
Routines: increase_size(), resize()

Needed modules

utility (realloc()): General purpose utility functions
metals (n_elements()): main module for handling metals

Types

type bubble_tree_types/n_pointer
Type fields
% p [bubble_node,pointer/optional/default=>null()]
type bubble_tree_types/bubble_node
Type fields
% l_box [real] :: size of the node

% l_ion (*) [real,allocatable] :: ionizing photon emission rate

% level [integer] :: tree-level where the node is located at

% m_out (*) [real,allocatable] :: metal mass

% mmetals_bub (*) [real,allocatable] :: metal mass

% n_bub [integer,optional/default=0]

% n_max [integer,optional/default=0]

% null [bubble_node,pointer]

% parent_node [bubble_node,pointer/optional/default=>]

% pop (*) [integer,allocatable] :: enriching population

% r_en (*) [real,allocatable] :: enriched radius

% r_ion (*) [real,allocatable] :: ionized radius

% sub_nodes (8) [n_pointer] :: pointers to the eight sub-nodes

% x (,) [real,allocatable] :: position

% x_node (3) [real] :: position of the corner of the node

% yields_bub (,) [real,allocatable]

Subroutines and functions

subroutine bubble_tree_types/resize(this)

resizes bubble storage for one node within the bubble tree

Parameters: this [real]

subroutine bubble_tree_types/increase_size(this)

Makes room for one additional bubble in a node of the bubble-tree

Parameters: this [real]
Called from: add_bubble_to_tree()

Stellar populations

Populations

Description

This module contains the type for storing IMFs, and the properties of the stellar populations

Quick access

Types: population
Variables: popii, popiii
Routines: compute_ionizing_mass_rate(), create_pop(), get_index_of_mass_cutoff(), init_stellar_populations(), massprobabilityimf(), output_pop(), set_yields()

Needed modules

numerical_parameters
resolution_parameters
stellar_props
utility: General purpose utility functions
imfs: This module contains intial mass functions
metals: main module for handling metals

Types

type populations/population
Type fields
% f_esc [real] :: ionizing photon escape fraction

% ion_m_rate (*) [real,allocatable] :: mass heating rate (M_sun/s) due to ionizing radiation

% mass (*) [real,allocatable] :: stellar mass in the bin

% n_bins [integer] :: number of bins

% n_cutoff [integer] :: Index in IMF array at which “massive” stars begin

% n_ion [real] :: Number of ionizing photons per stellar baryon

% p (*) [real,allocatable] :: relative likelihood to form star in the bin

% pop [integer] :: what stellar population is this?

% q (*) [real,allocatable] :: ionizing photon emission rate

% sn_energy (*) [real,allocatable] :: Energy of a SN in this bin (erg)

% sn_momentum (*) [real,allocatable] :: mass blown out SN (Msun cm/s)

% tlife (*) [real,allocatable] :: lifetime

% yields (,) [real,allocatable] :: mass blown out SN (Msun cm/s)

Variables

populations/popii [population,protected]
populations/popiii [population,protected]

Subroutines and functions

function populations/create_pop(n, pop)

Parameters

n [integer,in]
pop [integer,in]

Return

this [population]

Call to

massprobabilityimf(), get_index_of_mass_cutoff(), q_popii(), lifetime_popii(), e_sn_popii(), momentum_sn(), q_popiii(), lifetime_popiii(), e_sn_popiii()

subroutine populations/init_stellar_populations()

Called from: asloth

function populations/get_index_of_mass_cutoff(nstarmassbin, mstarpop)

This function determins the index where the desired mass is at in the mass bins.

Options: nstarmassbin [integer,in,optional/default=len(mstarpop)]
Parameters: mstarpop (nstarmassbin) [real,in]
Return: n_cutoff [integer]
Called from: create_pop()

subroutine populations/massprobabilityimf(p_starmassbin, mstarpop, pop, n_starmassbin)

This routine determins the mass probability of a mass bin. We determine the total stellar mass form in one step first and then draw individual stars from the IMF.

Parameters

p_starmassbin (n_starmassbin) [real,out]
mstarpop (n_starmassbin) [real,out]
pop [integer,in] :: PopII (2) or PopIII (3)?
n_starmassbin [integer,in]

Called from

create_pop()

Call to

assert()

subroutine populations/compute_ionizing_mass_rate(this)

Gas mass heating/ionizing rate from massive stars throughout their lifetimes. Gas density dependent.

Parameters: this [real]

subroutine populations/set_yields(this[, yields])

Parameters

this [real]
yields (,) [real,in,allocatable]

subroutine populations/output_pop(pop, fname)

Parameters

pop [real]
fname [character,in]

Called from

asloth

Initial mass functions

Description

This module contains intial mass functions

Quick access

Types: initial_mass_function, unknown_type
Routines: kroupa_cumulative_mass_df(), kroupa_cumulative_pdf(), kroupa_m_func(), kroupa_p_func(), kroupa_pdf(), m_func_dummy(), p_func_dummy(), power_law_mass_func(), power_law_p_func()

Needed modules

utility (assert()): General purpose utility functions

Types

type imfs/initial_mass_function

This type acts as a framework to implement additional IMFs. It cannot directly be used Rather, the idea is to create new types and inherit the basic structure from this one Note: This should be implemented as abstract type, but f2py.crackfortrant does not support these yet, which would break the documentation with sphinxfortran
type imfs/unknown_type
Type fields
% m_max [real]

% m_min [real]

% slope [real]
type imfs/unknown_type
Type fields
% m_max [real]

% m_min [real]

% m_turn1 [real,optional/default=0.08]

% m_turn2 [real,optional/default=0.5]

% slope1 [real,optional/default=-0.3]

% slope2 [real,optional/default=-1.3]

% slope3 [real,optional/default=-2.3]

Subroutines and functions

function imfs/m_func_dummy(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

function imfs/p_func_dummy(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

function imfs/power_law_mass_func(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

function imfs/power_law_p_func(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

function imfs/kroupa_p_func(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

Call to

assert(), kroupa_cumulative_mass_df()

function imfs/kroupa_m_func(this, m1, m2)

Parameters

this [real]
m1 [real,in]
m2 [real,in]

Return

res [real]

Call to

assert(), kroupa_cumulative_mass_df(), kroupa_cumulative_pdf()

function imfs/kroupa_cumulative_mass_df(this, m)

Parameters

this [real]
m [real,in]

Return

res [real]

Called from

kroupa_p_func(), kroupa_m_func()

function imfs/kroupa_pdf(this, m)

Parameters

this [real]
m [real,in]

Return

res [real]

function imfs/kroupa_cumulative_pdf(this, m)

Parameters

this [real]
m [real,in]

Return

res [real]

Called from

kroupa_m_func()

Stellar Properties

Quick access

Routines: e_sn_popii(), e_sn_popiii(), lifetime_popii(), lifetime_popiii(), momentum_sn(), q_popii(), q_popiii()

Needed modules

Subroutines and functions

function stellar_props/q_popiii(m)

Parameters: m [real,in]
Return: q [real]
Called from: create_pop()

function stellar_props/lifetime_popiii(m)

Parameters: m [real,in]
Return: lt [real]
Called from: create_pop()

function stellar_props/q_popii(m)

Parameters: m [real,in]
Return: q [real]
Called from: create_pop()

function stellar_props/lifetime_popii(m)

Parameters: m [real,in]
Return: lt [real]
Called from: create_pop()

function stellar_props/e_sn_popii(m)

Parameters: m [real,in]
Return: e_sn [real]
Called from: create_pop()

function stellar_props/e_sn_popiii(m)

Parameters: m [real,in]
Return: e_sn [real]
Called from: create_pop()

function stellar_props/momentum_sn(e_sn)

Parameters: e_sn [real,in]
Return: p_sn [real]
Called from: create_pop()

Python Scripts

scripts/plot_asloth.py

scripts.plot_asloth.plot_comparison(UserFolder, UserLabels=None)

Function to plot results from A-SLOTH and compare them to literature or other runs.

We use the most recent folder to plot and compare to literature. If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (list[str]) – List of folder names to be plotted.
UserLabels (list[str]) – List of labels. If none, UserFolders are used as labels

Returns: Beautiful plots

scripts.plot_asloth.readMWproperties(UserFolder)

scripts/tau/plot_Vion.py

tau.plot_Vion.plot_Vion(UserFolder, UserLabels, folder_output)

Function to plot ionisation histories and calculate tau

Parameters

UserFolder (List[str]) – List of folder names to be plotted
UserLabels (List[str]) – List of labels. If none, UserFolders are used as labels
folder_output (str) – Folder in which plots will be saved

Returns

tau0 (float) – optical depth to Thomson scattering
tau_sigma (float) – tau uncertainty

tau.plot_Vion.get_tau_sigma(folder)

Function to calculate tau without plotting ionisation history

Parameters

folder (str) – Folder in which plots will be saved

Returns

tau0 (float) – optical depth to Thomson scattering
tau_sigma (float) – tau uncertainty

scripts/wrapper

This script allows to explore input parameters by automatically launching various runs. Users can set their specific path_output variable. If you want to run with NBODY merger trees, also set path_tree. If you think that disk I/O might be a bottleneck, you should also set your username in the function N_disk_sleep() to work correctly.

wrapper.loop_trees.RunTrees(cats, NBODY)

This function loops over a set of merger trees and explores one combination of input parameters

We first define input parameters that are constant within this loop. Then, we define tree-specific input parameters. Then, we check if sufficient computing resources are available. Eventually, we launch the A-SLOTH run.

Parameters

cats (list) – catalogue of merger trees [[name, nlev, RAM]]
NBODY (bool) – N-Body or EPS-generated merger trees

wrapper.loop_trees.Get_CatList(NBODY=False, N=3)

Function to generate list of catalogues to loop over

Parameters

NBODY (bool) – N-Body or EPS-generated merger trees
N (int) – number of random realisations in case of EPS-generated trees

Returns

cats – list of lists of type [[name, nlev, RAM]]

Return type

list

wrapper.loop_trees.avail_mem_GB(): Funtion returns available RAM in GB

wrapper.loop_trees.CPU_use(): Funtion returns available CPUs in %

wrapper.loop_trees.N_disk_sleep()

Funtion returns number of processes that are idle due to I/O.

Change username accordingly

wrapper.analyse_trees.get_p_from_folders(dir_prefix, ifEPS=True)

Function that calculates the p-value (and additional values).

Based on the directory prefix for the A-SLOTH output folder that should be analysed Function will you all folders that match this directory prefix.

Parameters: dir_prefix (str) – directory prefix that should be used to search for data
Returns: fit_asloth – class object that contains fit results
Return type: asloth_fit

wrapper.tutorial_GridPlot.plot_tutorial2()

Plotting script to visualise results of the two scripts loop_scripts.py and analysis_trees.py.

The plot layout is not optimized for aesthetics, but it should simply demonstrate the versatility of A-SLOTH. The resulting plot is a modified version of Fig. 17 in Hartwig+22

scripts/tau/plot_tgas.py

utilities.plot_tgas.plot_tgas(output_folder)

Illustrates time evolution of different baryonic quantities based on output file t_gas_substeps.dat. outout_folder has to be set manually.

Parameters: output_folder (str) – folder that contains results of run that should be analysed

scripts/utility.py

class utilities.utility.asloth_config

container class for writing config namelist files for ASLOTH

set_config_to_default()

sets default parameters for the main configuration

Parameters: self (asloth_config) – the configuration to be restored to defaults

set_metals(metals)

sets metal configuration for a specified list of elements

Parameters

self (asloth_config) – configuration
metals (list) – list of strings of the elements to be traced

to_file(fname)

class utilities.utility.asloth_fit

class for handling fitting parameters and results of A-SLOTH

get_p(): ‘ Calculates the goodness-of-fit parameters based on 6 different observables. Please see Hartwig+22 for more details.

print_fit(): ‘ Prints all class properties

set_fit_to_default()

sets default parameters for the fit object

Parameters: self (asloth_fit) – set all parameters to default values

utilities.utility.get_p_from_sigma(sigma): Get p-value from “how many sigma away”

utilities.utility.write_namelist(f, list_name, var_dict)

function to write fortran namelists

Parameters

f (file) – opened file to write the namelist to
list_name (str) – name of the namelist. Note that this must be the exact name used in the fortran code
var_dict (dictionary) – dictionary of variables to be written to the file

scripts/SMHM/plot_scatterSMHM.py

SMHM.plot_scatterSMHM.plot_scatterSMHM(UserFolder, UserLabels, folder_output)

Function to read data and plot scatter SMHM relation.

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels.
folder_output (str) – where the plot is saved.

Returns

None

SMHM.plot_scatterSMHM.plot_CumuSMF(UserFolder, UserLabels, folder_output, if_plot)

Function to return ks statistics, p-values, and MW stellar masses

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels.
folder_output (str) – where the plot is saved.

Returns

Lists of ks statistic, p-value and MW stellar mass.

SMHM.plot_scatterSMHM.read_data(UserFolder, UserLabels, folder_output)

Function to read data to plot SMHM relation

If one or more folders are specified, they will all be plotted. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels
folder_output (str) – where the plot is saved

Returns

Virial masses and stellar masses of galaxies in pandas.DataFrame format

SMHM.plot_scatterSMHM.M_AM(M)

Function to compute expected stellar mass of the galaxy at given virial mas (Garrison-Kimmel et al. 2014).

Parameters: M (float) – virial mass of the halo
Returns: expected stellar mass

SMHM.plot_scatterSMHM.f_bwc(x)

SMHM.plot_scatterSMHM.AMtwodhistogram(df_list, y0, p_dir, output_name, WhichMass, climmax, if_imfana)

The actual function to plot scatter SMHM relation.

Parameters

df_list (list of pd.DataFrame) – input data
y0 (float) – the observational completeness
p_dir (str) – output folder

Returns:

SMHM.plot_scatterSMHM.CumulativeNumberOfSatellites(df_list, output_name, WhichMass, if_plot, p_dir)

The actual function to plot cumulative stellar mass function and compute ks statistic, p-value from the KS test.

Parameters

df_list (list of pd.DataFrame) – input data
output_name (str) – figure name
WhichMass (float) – which stellar mass to use (the total or survival)
if_plot (bool) – whether to make plots or not
p_dir (str) – output folder

Returns

lists of ks statistic, p-value, MW stellar mass

scripts/SMHM/plot_binnedSMHM.py

SMHM.plot_binnedSMHM.plot_binnedSMHM(UserFolder, UserLabels, folder_output)

The function reads data from multiple folders and plot the binned SMHM relations

If one or more folders are specified, they will all be plotted as separate SMHM relations. This can also be used to plot just one specific folder.

Parameters

UserFolder (str) – List of folder names to be plotted.
UserLabels (str) – List of labels. If none, UserFolders are used as labels
folder_output (str) – where the plot is saved

Returns

plot of SMHM relation comparison

SMHM.plot_binnedSMHM.IMFANA(AMBound, MX, MAMX, imf_list, y0, p_dir, imfname, WhichMass, climmax, cps, cs, ilabel, fname)

The function plots binned SMHM relation.

Parameters

AMBound – boundary of the SMHM relation from abundance matching (AM)
MX – virial masses
MAMX – stellar masses obtained from the AM technique at given virial masses
imf_list (list of pd.DataFrame) – input data
y0 (float) – observational completeness
p_dir (str) – output folder

Returns

plot of SMHM relation

SMHM.plot_binnedSMHM.AM_BinMeanstd(df_list, WhichMass, tname, index_data, c=None)

The actual function to plot binned SMHM relation.

Parameters

df_list (list of pd.DataFrame) – input data
WhichMass (float) – which stellar mass to use (the total or survival)
tnmae (str) – labels
index-data – index of the data
c – specific color for the SMHM relation

Returns

adding a binned SMHM relation to the plot

SMHM.plot_binnedSMHM.PlotNadler20SMHM(): The function plots SMHM relation from Nadler et al. 2020.

scripts/plot_mcrit.py

utilities.plot_mcrit.plot_mcrit()

Function to plot ciritical mass of a halo at certain redshift, given by different models, and at different initial streaming velocities.

Returns: Figure that shows M_crit v.s. z at different initial streaming velocities and from different models

utilities.plot_mcrit.crit_mass(z, lw_mode, VBC)

Function to obtain ciritical mass of a halo at certain redshift, given by different models

We use lw_mode=5 (Schauer et al. 2021) as the fiducial model. Use can also choose lw_mode=4, which is a combined model from O’Shea et al. 2008, Stacy et al. 2011, and Hummel et al. 2012. lw_mode=7 corresponds to a model from Fialkov et al. 2013.

Parameters

z (float) – redshift
lw_mode (int) – which M_crit model
VBC (float) – initial streaming velocity, in units of sigma_v

Returns

Critical mass of a halo to have star formation

scripts/plot_stellarprop.py

utilities.plot_stellarprop.plot_stellarproperties()

Function to reproduce the stellar properties plot

Returns: Stellar masses v.s. stellar properties, e.g. stellar lifetime, carbon yields, iron yields, supernova energie.

Usage Policy

You can use A-SLOTH freely for scientific research and other applications in accordance with the license. If you publish results for which you have used A-SLOTH, you must cite the following two papers:

Hartwig et al. 2022, submitted to ApJ
Magg et al. 2022, submitted to JOSS

You do not have to invite the A-SLOTH developers as co-authors on your publications. If you wish to contribute to A-SLOTH, please get in contact with us and/or create a pull-request.

Help

If you encounter any problems with A-SLOTH, there are several options to help you:

Check the section on debugging.
Create an issue on GitLab.
Contact the developers: hartwigATu-tokyo.ac.jp

A-SLOTH Documentation

Getting Started

System Requirements and Dependencies

Tutorials

Tutorial 1: Hello A-SLOTH

Tutorial 2: Vary one input parameter manually

Tutorial 3: Explore two input parameters automatically

Tutorial 4: Analyse evolution of baryons in one merger tree branch

Tutorial 5: Output additional information from A-SLOTH

Configure your simulation

How Tos

Debugging

Compiler Options

Python Scripts

Additional Output

Common Issues

Git clone does not work

The code does not compile

The generation of the dependency list fails

A-SLOTH does not accept my parameter.nml file

The code crashes with a strange memory error

Compilation with ifort crashes

A-SLOTH crashes with end of file error

The standard python plotting script does not work

Changing which chemical elements are traced

Get a tree

Check Changes

References and Links

Articles by the ASLOTH collaboration

The Caterpillar Project

List of Modules and Procedures

Main Code

ASLOTH main program

Configuration

config

Type definitions

defined_types

Tree Reader

tree_reader

Tree initialization

tree_initialization

numerical and resolution parameters

numerical parameters

resolution parameters

Precomputed time-dependent quantities

crit_masses

cosmic_time

EPS wrapper

eps_wrapper

Chemistry

chemistry

Input files

Memory modules

Tree_memory_arrays

Tree_memory_arrays_passable

Star formation

SF_routines

Star_formation

Stellar feedback

feedback_routines

snowplow

Feedback tracing

feedback_arrays

volume_fractions

Outputs

to_file

Utility modules

Converters

Savitzki-Golay filter

Utility

Virial radius

Check flags

Random number generator

Metal modules

Metals

Read Yields

Metal functions

IGM Metallicity routines

Metal Mix dZ

Metallicity distribution function