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.
The easiest way to install the package is using pip:
$ pip install starmatrixThis 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 starmatrixIf 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.
To test installation worked you can just run Starmatrix with default values running:
$ starmatrix --generate-config
$ starmatrix --config config-example.ymlThe 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.
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]"
$ pytestUse starmatrix running:
$ starmatrix --config FILENAMEwhere 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, expressed as stellar mass fractions.
You can find the complete documentation at ReadTheDocs' Starmatrix page.
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
snia_m_max # Upper mass limit for binaries with SN Ia. Default value: 16 Msun
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-configThat command will create a config-example.yml file in the current dir containing the default values.
Starmatrix uses solar mass (M*) as the reference unit for all quantities, so internally the upper and lower limits for IMF are expressed in solar masses, Delay Time Distributions are expressed as [Supernovae per Year per M*] and the expelled elements file is expected to express data as expelled mass per solar mass, same as the supernova yields.
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]).
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.
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. (2021)
- 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)
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
- sei2013
Data from Seitenzahl et al. 2013
- ln2018-1
Data from Leung & Nomoto 2018, Tables 6/7
- ln2018-2
Data from Leung & Nomoto 2018, Tables 8/9
- ln2018-3
Data from Leung & Nomoto 2018, Tables 10/11
- ln2020
Data from Leung & Nomoto 2020
- br2019-1
Data from Bravo, E. et al., Table 3
- br2019-2
Data from Bravo, E. et al., Table 4
- gro2021-1
Data from Gronow, S. et al., Tables 3/A10 He+Core detonations
- gro2021-2
Data from Gronow, S. et al., Tables 4/A8 He+Core detonations
- mor2018-1
Data from Mori, K. et al., W7
- mor2018-2
Data from Mori, K. et al., WDD2
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.
If you find Starmatrix helpful, please consider citing:
@article{Bazan2022,
doi = {10.21105/joss.04461},
url = {https://doi.org/10.21105/joss.04461},
year = {2022},
publisher = {The Open Journal},
volume = {7},
number = {75},
pages = {4461},
author = {Juanjo Bazán and Mercedes Mollá},
title = {Starmatrix: Modelling nucleosynthesis of galactic chemical elements},
journal = {Journal of Open Source Software}
}Copyright © 2022 Juanjo Bazán, released under the MIT license.
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., 2021, MNRAS V501, 3, 3122–3136
- 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 656, A94
- Mori, K. et al., 2018, ApJ, 863:176
- Chen, X., Hu, L. & Wang, L., 2021, ApJ, 922, 15
- Strolger et al, 2020, ApJ, Vol 890, 2. doi: 10.3847/1538-4357/ab6a97
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