Skip to main content

A Python Package for Galaxy Cold Molecular Gas and Star Formation Evolution Equations.

Project description

A simple introduction:

This Python package provides functions to calculate a galaxy’s cold molecular gas mass to stellar mass ratio (gas fraction), cold molecular gas depletion time and galaxy main-sequence star formation rate.

The motivation is that the evolution of star-forming galaxies’ star formation rate (SFR) and cold molecular gas reservoir have now been reasonably well measured out to very high redshift (z~6), from present time up to as early as one giga-year after the Big Bang (Madau & Dickinson 2014; Genzel et al. 2015; Scoville et al. 2016, 2017; Tacconi et al. 2018; Liu et al. 2018, 2019). These studies have found that the majority of galaxies have a steady and parametrizable evolution in their stellar mass growth, SFR, and molecular gas mass (or molecular gas to total baryon fraction, i.e., gas fraction). These evolution functions have provided crucial constraints to cosmological simulations of dark matter halo evolution and the semi-analytic modeling of the simulated galaxy evolution in the dark matter halo (e.g., Popping et al. 2014ab, 2016, 2017, 2019ab). However, currently there are many parametrizations (or we say “equations”) in the literature and each has its own limitation which is not very well aware by the generic users. Therefore, we provide this Python package which contains as many galaxy gas, dust, star formation and stellar mass evolution equations as possible for easier comparison and study.

A simple usage:

To get cold molecular gas density evolution curve, e.g., Fig. 15 of D. Liu et al. (2019b)

import a3cosmos_gas_evolution
a3cosmos_gas_evolution.help()
z, rho_mol_gas = a3cosmos_gas_evolution.get_cosmic_mol_gas_density_A3COSMOS() # return rho_mol_gas in solar mass per cubic mega parsec.
z, rho_mol_gas = a3cosmos_gas_evolution.get_cosmic_mol_gas_density_Tacconi2018() # return rho_mol_gas in solar mass per cubic mega parsec.
z, rho_mol_gas = a3cosmos_gas_evolution.get_cosmic_mol_gas_density_Scoville2017() # return rho_mol_gas in solar mass per cubic mega parsec.
a3cosmos_gas_evolution.plot_cosmic_mol_gas_density() # or plot it with matplotlib

To compute gas fraction, i.e., f_gas = M_molgas / (M_molgas + M_star)

import a3cosmos_gas_evolution
a3cosmos_gas_evolution.help()
a3cosmos_gas_evolution.calc_gas_fraction_A3COSMOS(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=True) # or we can input cosmic_age = 2.178 instead of z = 3.0
a3cosmos_gas_evolution.calc_gas_fraction_Tacconi2018(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=True)
a3cosmos_gas_evolution.calc_gas_fraction_Scoville2017(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=True)

To compute gas-to-stellar mass ratio, i.e., mu_gas = M_molgas / M_star

import a3cosmos_gas_evolution
a3cosmos_gas_evolution.help()
a3cosmos_gas_evolution.calc_gas_fraction_A3COSMOS(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=False) # or we can input cosmic_age = 2.178 instead of z = 3.0
a3cosmos_gas_evolution.calc_gas_fraction_Tacconi2018(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=False)
a3cosmos_gas_evolution.calc_gas_fraction_Scoville2017(z = 3.0, lgMstar = 10.5, DeltaMS = 0.5, return_fgas=False)

To compute gas depletion time, i.e., M_molgas / SFR

import a3cosmos_gas_evolution
a3cosmos_gas_evolution.help()
a3cosmos_gas_evolution.calc_gas_depletion_time_A3COSMOS(z = 3.0, lgMstar = 10.5, DeltaMS = 0.0)
a3cosmos_gas_evolution.calc_gas_depletion_time_Tacconi2018(z = 3.0, lgMstar = 10.5, DeltaMS = 0.0)
a3cosmos_gas_evolution.calc_gas_depletion_time_Scoville2017(z = 3.0, lgMstar = 10.5, DeltaMS = 0.0)

To compute galaxy main-sequence star formation rate, i.e., SFR_MS

import a3cosmos_gas_evolution
a3cosmos_gas_evolution.help()
a3cosmos_gas_evolution.calc_SFR_MS_Speagle2014(z = 3.0, lgMstar = 10.5)

Acknowledgement:

Please cite Liu et al. (2019b) and/or https://ascl.net/1910.003 if you would like to use this package for your research. Thank you.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

a3cosmos_gas_evolution-2.1.0.tar.gz (9.3 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

a3cosmos_gas_evolution-2.1.0-py3-none-any.whl (119.8 kB view details)

Uploaded Python 3

File details

Details for the file a3cosmos_gas_evolution-2.1.0.tar.gz.

File metadata

  • Download URL: a3cosmos_gas_evolution-2.1.0.tar.gz
  • Upload date:
  • Size: 9.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.11

File hashes

Hashes for a3cosmos_gas_evolution-2.1.0.tar.gz
Algorithm Hash digest
SHA256 1d3460a593088daaa0117038df3637aec715cacd80f8de1df919ccbc15919fbc
MD5 f08d045e69c67a6a95690612b8c794ee
BLAKE2b-256 f55caa31f8d8cf7bc95f71eb4521c7f8615a51ced2afed763cb0030464c6e2d1

See more details on using hashes here.

File details

Details for the file a3cosmos_gas_evolution-2.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for a3cosmos_gas_evolution-2.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 66854010589b124a5e44424c7640261f805740269fa2316805a7bc1732b7f05d
MD5 8dc7cb3352dfe88cf3f7754367247a76
BLAKE2b-256 81f3e79ae046c8aef7a2522faf356f73dc5002f15631d641b669ac0fe9539fe1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page