starmatrix 1.7.1

Modelling nucleosynthesis of galactic chemical elements using Q-Matrices

Starmatrix is a Q-Matrices generator.

Based on explicit values for solar abundances, Z and IMF, Starmatrix calculates matrices Q(i,j) of masses of elements i ejected to the galactic medium as element j, for a complete range of stellar masses, accounting for supernovae of types Ia and II. You can read more about the Matrices Q formalism in Ferrini et al. 1992.

Starmatrix computes the contribution matrix of 15 elements:

H

D

He3

He4

C

C13

N

O

n.r.

Ne

Mg

Si

S

Ca

Fe

Starmatrix is a tool for astronomers working with galactic chemical evolution models needing detailed datasets to use as input, wanting to compare the validity of different yield sets or assessing different nucleosynthesis modeling assumptions.

Installation

The easiest way to install the package is using pip:

$ pip install starmatrix

This will install the most recent release version and also some dependencies if they are not found in the system: numpy, scipy and pyyaml

A previous installation can be upgraded to the latest version with:

$ pip install --upgrade starmatrix

Edge

If you want to play with the latest code present in this repository even if it has not been released yet, you can do it by cloning the repo locally and instructing pip to install it:

$ git clone https://github.com/xuanxu/starmatrix.git
$ cd starmatrix
$ pip install -e .

Python >= 3.7 is required.

Test installation

To test installation worked you can just run Starmatrix with default values running:

$ starmatrix --generate-config
$ starmatrix --config config-example.yml

The first command will generate a basic configuration file (named config-example.yml) and the second command will run Starmatrix using that configuration. A folder named results should be created with the output files.

Test suite

Starmatrix includes a test suite located in the /src/starmatrix/tests directory. The current state of the build is publicly tracked by GitHub CI. You can run the latest tests locally and get information on code coverage if you clone the code to your local machine, install its development dependencies and use pytest:

$ git clone https://github.com/xuanxu/starmatrix.git
$ cd starmatrix
$ pip install -e ".[dev]"
$ pytest -v --cov=starmatrix

Usage

Use starmatrix running:

$ starmatrix --config FILENAME

where FILENAME is the path to the config yaml file.

Running starmatrix will produce a directory with three output files:

• mass_intervals: all the mass intervals used to integrate for all the mass range

• imf_supernova_rates: the initial mass functions for the supernova rates for each mass interval

• qm-matrices: the Q(m) matrices for every mass interval defined in the mass_intervals file

You can find the complete documentation at ReadTheDocs’ Starmatrix page.

Input params

Starmatrix reads a config file where several options can be set in yaml format:

z: 0.0200               # metallicity
sol_ab: as09            # solar abundances
imf: kroupa2002         # initial mass function (IMF)
imf_m_low: 0.15         # lower mass limit for the IMF
imf_m_up: 100           # upper mass limit for the IMF
total_time_steps: 300   # number of time steps (will result in a Q Matrix per step)
m_min: 0.98             # min value for stellar mass
m_max: 40               # max value for stellar mass
binary_fraction: 0.15   # rate of binary stars
dtd_sn: rlp             # delay time distribution for supernovae
sn_yields: iwa1998      # Dataset for Supernovae yields
output_dir: results     # Name of the directory where results are written.
integration_step: logt  # The integration step can be constant in t, constant in log(t), or custom.
expelled_elements_filename: ejecta.txt  # Filename of ejected data.

Starmatrix will use its internal default values for all params for which no values are provided.

If you want to use an existent configuration file as template for your own, you can run:

$ starmatrix --generate-config

That command will create a config-example.yml file in the current dir containing the default values.

Initial mass function

The imf param in the config file can be set to use any of the predefined IMFs from different papers/authors:

salpeter:

Salpeter 1955

starburst:

Starburst 1999 (a Salpeter with mass limits in [1, 120])

miller_scalo:

Miller & Scalo 1979

ferrini:

Ferrini, Palla & Penco 1998

kroupa2001:

Kroupa 2001

kroupa2002:

Kroupa 2002

chabrier:

Chabrier 2003

maschberger:

Maschberger 2012

The default value is kroupa2002. If you want to use your own IMF you can do so subclassing the IMF class.

The IMF will be normalized integrating in the [imf_m_low, imf_m_up] mass interval (default: [0.15, 100], except Starburst: [1, 120]).

Solar abundances

The sol_ab param in the config file can be set to use any of the available abundances datasets from different papers/authors:

ag89:

Anders & Grevesse 1989

gs98:

Grevesse & Sauval 1998

as05:

Asplund et al. 2005

as09:

Asplund et al. 2009

he10:

Heger 2010

lo19:

Lodders et al. 2019

The default value is as09. If you want to use your own abundances data you can do so subclassing the Abundances class.

Delay Time Distributions

The dtd_sn param in the config file can be set to use any of the available Delay Time Distributions for supernova rates from different papers/authors:

rlp:

Supernova rates from Ruiz-Lapuente et al. (2000)

maoz:

DTD of Type Ia supernovae from Maoz & Graur (2017)

castrillo:

DTD of Type Ia supernovae from Castrillo et al. (2020)

greggio:

DTD of Type Ia supernovae from Greggio, L. (2005)

chen:

DTD of Type Ia supernovae from Chen et al. (2021)

greggio-CDD04:

DTD from model Close DD 0.4 Gyrs from Greggio, L. (2005)

greggio-CDD1:

DTD from model Close DD 1 Gyr from Greggio, L. (2005)

greggio-WDD04:

DTD from model Wide DD 0.4 Gyrs from Greggio, L. (2005)

greggio-WDD1:

DTD from model Wide DD 1 Gyr from Greggio, L. (2005)

greggio-SDCH:

DTD from model SD Chandra from Greggio, L. (2005)

greggio-SDSCH:

DTD from model SD sub-Chandra from Greggio, L. (2005)

strolger-fit1:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (10, 600, 220)

strolger-fit2:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (110, 1000, 2)

strolger-fit3:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (350, 1200, 20)

strolger-fit4:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (6000, 6000, -2)

strolger-fit5:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (-650, 2200, 1100)

strolger-optimized:

Phi function from Strolger et al. (2020) with (ξ, ω, 𝛼) = (-1518, 51, 50)

Supernovae yields

The sn_yields param in the config file can be set to use any of the available supernova yields datasets from different papers/authors:

iwa1998:

Data from Iwamoto, K. et al., 1999, ApJ 125, 439

sei2013:

Data from Seitenzahl et al. 2013, MNRAS 429, 2, 1156–1172

ln2018-1:

Data from Leung & Nomoto 2018, ApJ, Vol 861, Issue 2, Id 143, Tables 6/7

ln2018-2:

Data from Leung & Nomoto 2018, ApJ, Vol 861, Issue 2, Id 143, Tables 8/9

ln2018-3:

Data from Leung & Nomoto 2018, ApJ, Vol 861, Issue 2, Id 143, Tables 10/11

ln2020:

Data from Leung & Nomoto 2020, ApJ, Vol 888, Issue 2, Id 80

br2019-1:

Data from Bravo, E. et al., 2019, MNRAS, 482, Issue 4, 4346–4363, Table 3

br2019-2:

Data from Bravo, E. et al., 2019, MNRAS, 482, Issue 4, 4346–4363, Table 4

gro2021-1:

Data from Gronow, S. et al., 2021, A&A, Tables 3/A10 He+Core detonations

gro2021-2:

Data from Gronow, S. et al., 2021, A&A, Tables 4/A8 He+Core detonations

mor2018-1:

Data from Mori, K. et al., 2018, ApJ, 863:176 W7

mor2018-2:

Data from Mori, K. et al., 2018, ApJ, 863:176 WDD2

Contributions

If you find a bug or have a question, please [open an issue in the project’s repo](https://github.com/xuanxu/starmatrix/issues).

Contributions are welcome, please read our contributing guidelines.

License

Copyright © 2022 Juanjo Bazán, released under the MIT license.

Credits

Starmatrix is built upon a long list of previous works from different authors/papers:

• Ferrini et al., 1992, ApJ, 387, 138

• Ferrini & Poggiantti, 1993, ApJ, 410, 44F

• Portinari, Chiosi & Bressan, 1998,AA,334,505P

• Talbot & Arnett, 1973, ApJ, 186, 51-67

• Galli et al., 1995, ApJ, 443, 536G

• Mollá et al., 2015, MNRAS, 451, 3693-3708

• Iwamoto et al., 1999, ApJS, 125, 439

• Seitenzahl et al., 2013, MNRAS, Volume 429, Issue 2, 1156–1172

• Matteucci & Greggio, 1986, A&A, 154, 279M

• Mollá et al., 2017, MNRAS, 468, 305-318

• Gavilan, Mollá & Buell, 2006, A&A, 450, 509

• Raiteri C.M., Villata M. & Navarro J.F., 1996, A&A 315, 105-115

• Ruiz-Lapuente, P., Canal, R., 2000, astro.ph..9312R

• Maoz, D. & Graur, O., 2017, ApJ, 848, 25M

• Castrillo, A. et al., 2020, MNRAS

• Greggio, L., 2005, A&A 441, 1055–1078

• Leung & Nomoto, 2018, ApJ, Vol 861, Issue 2, Id 143

• Leung & Nomoto, 2020, ApJ, Vol 888, Issue 2, Id 80

• Bravo, E. et al., 2019, MNRAS, 482, Issue 4, 4346–4363

• Gronow, S. et al., 2021, A&A

• Mori, K. et al., 2018, ApJ, 863:176

• Chen, X., Hu, L. & Wang, L., 2021, ApJ

• Strolger et al, 2020, ApJ, Vol 890, 2. doi: 10.3847/1538-4357/ab6a97