Welcome to PST’s Documentation
Overview
The Population Synthesis Toolkit (PST) is a Python library intended for astronomy researchers, particularly those working in extragalactic astrophysics and stellar population studies, who need a flexible and extensible Python-based toolkit for modeling galaxy properties. PST is designed to provide a user-friendly interface for working with Simple Stellar Population (SSP) models and for synthesizing a variety of observable quantities such as spectra, photometry, and equivalent widths. In particular, PST is conceived to address the following challenges: - Handling a broad variety of SSP libraries, publicly available in heterogeneous native formats. - Modeling arbitrarily complex galaxy star formation and chemical evolution histories. - Enabling the simultaneous and self-consistent analysis of photometric and spectroscopic data from different instruments.
At its core, PST combines individual SSPs to generate Composite Stellar Populations (CSP) through the implementation of Chemical Evolution Models (CEM). These models track the evolution of a stellar system, considering both its Star Formation History (SFH)—the mass converted into stars over time—and the chemical enrichment of the gas.
PST supports a wide range of SSP models (e.g., PopStar, XSL, MILES, BC03) and offers flexible CEMs, allowing users to simulate observable quantities with precision. Additionally, it provides auxiliary tools to include dust extinction and kinematics effects.
Key Features
Intuitive interface to multiple SSP models (e.g., PopStar, XSL, MILES, BC03).
Implementation of various Chemical Evolution Models (CEMs) to simulate Composite Stellar Populations (CSPs).
Tools to compute synthetic spectra, photometry, and equivalent widths from both SSPs and CSPs.
Optional integration of dust extinction and kinematics effects into the model outputs.
Modules for additional functionality, such as fitting Spectral Energy Distributions (SEDs) to observed data.
Acknowledgement
If you use PST in your research, please cite the following paper:
@article{PST_2025,
doi = {10.21105/joss.08203},
url = {https://doi.org/10.21105/joss.08203},
year = {2025},
publisher = {The Open Journal},
volume = {10},
number = {111},
pages = {8203},
author = {Corcho-Caballero, Pablo and Ascasibar, Yago and Jiménez-López, Daniel},
title = {The Population Synthesis Toolkit (PST) Python Library},
journal = {Journal of Open Source Software} }