Merge pull request #74 from ivastar/editorial

Editorial comments paper.md

Author: PabloCorcho

paper/paper.md

@@ -1,5 +1,5 @@
 ---
-title: 'The Population Synthesis Toolkit (PST) Python library'
+title: 'The Population Synthesis Toolkit (PST) Python Library'
 tags:
   - Python
   - astronomy
@@ -38,56 +38,52 @@ bibliography: paper.bib
 
 # Summary
 
-Stellar population synthesis is a crucial methodology in astrophysics, enabling the interpretation of the integrated light of galaxies and stellar clusters. By combining empirical and/or theoretical libraries of the spectral energy distribution emitted by simple stellar populations (SSPs) with models of the star formation history (SFH) and chemical evolution, population synthesis facilitates the estimation of essential galaxy properties, such as total stellar mass, star formation rate, mass-weighted age and metallicity, etc.
+Stellar population synthesis is a crucial methodology in astrophysics, enabling the interpretation of the integrated light of galaxies and stellar clusters. By combining empirical and/or theoretical libraries of the spectral energy distributions emitted by simple stellar populations (SSPs) with star formation history (SFH) and chemical evolution models, population synthesis can help estimate essential properties of galaxies, such as total stellar mass, star formation rate, mass-weighted age, metallicity, and so on.
 
-PST is a Python library that offers a comprehensive and flexible framework for stellar population synthesis. Its main goal is to compute composite spectra using different galaxy evolution models and SSP libraries with ease and efficiency. It incorporates additional effects, such as cosmic redshift and dust extinction and attenuation, and it computes several observable quantities derived from the spectra, including broadband photometric fluxes and equivalent widths.
+PST is a Python library that offers a comprehensive and flexible framework for stellar population synthesis. Its main goal is to easily and efficiently compute composite spectra using different galaxy evolution models and SSP libraries. It also incorporates additional effects such as cosmic redshift, dust extinction and attenuation, and computes several observable quantities derived from the spectra, including broadband photometric fluxes and equivalent widths.
 
 # State of the field
 
-A number of software packages have been developed to support stellar population synthesis and modeling of galaxy spectral energy distributions. Tools such as [binary_c-python](https://gitlab.surrey.ac.uk/ri0005/binary_c-python) and [SPISEA](https://github.com/astropy/SPISEA) [@hosek+20] are primarily designed for generating and analyzing simple stellar populations, often with a focus on individual stars, binaries, or star clusters. Meanwhile, libraries such as [python-FSPS](https://github.com/dfm/python-fsps), a Python interface to the Flexible Stellar Population Synthesis (FSPS) code [@conroy+09], and the more recent [DSPS](https://github.com/ArgonneCPAC/dsps) [@hearin+23], implemented using JAX for efficient gradient computation and forward modeling, provide extensive modeling capabilities, although they are sometimes limited to a specific set of SSP models or isochrones.
+A number of software packages have been developed to support stellar population synthesis and modeling of galaxy spectral energy distributions. Tools such as [binary_c-python](https://gitlab.surrey.ac.uk/ri0005/binary_c-python) and [SPISEA](https://github.com/astropy/SPISEA) [@hosek+20] are designed primarily for generating and analyzing simple stellar populations, often with a focus on individual stars, binaries, or star clusters. Meanwhile, libraries such as [python-FSPS](https://github.com/dfm/python-fsps), a Python interface to the Flexible Stellar Population Synthesis (FSPS) code [@conroy+09], and the more recent [DSPS](https://github.com/ArgonneCPAC/dsps) [@hearin+23], implemented using JAX for efficient gradient computation and forward modeling, provide extensive modeling capabilities, although they are sometimes limited to a specific set of SSP models or isochrones.
 
-Other packages put a stronger emphasis on fitting observed data to derive galaxy properties. These include Bayesian frameworks like [CIGALE](https://cigale.lam.fr/) [@boquien+19], [ProSpect](https://github.com/asgr/ProSpect) [@robotham+20] or [Prospector](https://prospect.readthedocs.io/en/v1.0.0/) [@johnson+21], which infer star formation histories and other physical parameters using spectro-photometric data. Alternative frequentist tools such as [PpXF](https://pypi.org/project/ppxf/) [@capellari+04], [Starlight](http://www.starlight.ufsc.br/) [@cid-fernandes+05], or [Pipe3D](https://gitlab.com/pipe3d/pyPipe3D) [@sanchez+16], are commonly used to extract stellar kinematics and stellar population parameters from observed galaxy spectra, often in the context of integral field spectroscopy.
+Other packages put a stronger emphasis on fitting observed data to derive galaxy properties. Examples of these include  Bayesian frameworks such as [CIGALE](https://cigale.lam.fr/) [@boquien+19], [ProSpect](https://github.com/asgr/ProSpect) [@robotham+20] and [Prospector](https://prospect.readthedocs.io/en/v1.0.0/) [@johnson+21], which infer star formation histories and other physical parameters using spectro-photometric data. Alternative frequentist tools such as [PpXF](https://pypi.org/project/ppxf/) [@capellari+04], [Starlight](http://www.starlight.ufsc.br/) [@cid-fernandes+05], or [Pipe3D](https://gitlab.com/pipe3d/pyPipe3D) [@sanchez+16], are commonly used to extract stellar kinematics and stellar population parameters from observed galaxy spectra, often in the context of integral field spectroscopy.
 
 # Statement of need
 
-Compared to alternative approaches, the user-friendly modular framework of PST is conceived to address the following challenges:
+The user-friendly modular framework of PST is designed to address the following challenges:
 
-- To handle a broad variety of SSP libraries, publicly available in heterogeneous native formats.
-- To model arbitrarily complex galaxy star formation and chemical evolution histories.
-- To enable the simultaneous and self-consistent analysis of photometric and spectroscopic data from different instruments.
+- Handle a broad variety of SSP libraries that are publicly available in heterogeneous native formats.
+- Model arbitrarily complex galaxy star formation and chemical evolution histories.
+- Enable the simultaneous and self-consistent analysis of photometric and spectroscopic data from different instruments.
 
-PST is designed for astronomy researchers, particularly those working in extragalactic astrophysics and stellar population synthesis, who need a flexible and extensible Python-based toolkit for modeling galaxy properties. It is suited for users with intermediate to advanced expertise in Python and familiarity with common data formats and concepts in astronomical spectroscopy and photometry.
+PST is designed for astronomy researchers, especially those working in extragalactic astrophysics and stellar population synthesis, who require a flexible and extensible Python-based toolkit for modeling galaxy properties. PST is suited for users with intermediate to advanced Python expertise and familiarity with common data formats and concepts in astronomical spectroscopy and photometry.
 
-Primary use cases include data analysis, synthetic model construction, and pipeline integration for studies involving stellar population synthesis (see examples below). PST is especially valuable in workflows that combine observational data with theoretical models in a Bayesian or forward-modeling context.
+The primary use cases are data analysis, synthetic model construction, and pipeline integration for studies involving stellar population synthesis (see the examples below). PST is particularly useful in workflows that combine observational data with theoretical models within a Bayesian or forward-modeling framework.
 
-PST is currently a dependency of PyKOALA [@pykoala], another open source Python package focused on the reduction of optical integral-field spectroscopic observations, where it is mainly used to derive broadband photometry.
-It is also at the core of the Bayesian Estimator for Stellar Population Analysis [[BESTA](https://https://besta.readthedocs.io/), see also @cc+25], where it is coupled with the [CosmoSIS](https://cosmosis.readthedocs.io/en/latest/) [@zuntz+15] Monte Carlo sampling framework to infer the physical properties of galaxies from the observed colours and spectra.
+PST is currently a dependency of PyKOALA [@pykoala], another  open-source Python package focused on reducing optical integral-field spectroscopic observations. There, PST is mainly used to derive broadband photometry.
+PST is also at the core of the Bayesian Estimator for Stellar Population Analysis [[BESTA](https://https://besta.readthedocs.io/), see also @cc+25], where it is coupled with the [CosmoSIS](https://cosmosis.readthedocs.io/en/latest/) [@zuntz+15] Monte Carlo sampling framework to infer the physical properties of galaxies from the observed colors and spectra.
 
 # Features and functionality
 
-PST design is built around three main components:
+PST design is built around three main components.
 
-First, the `SSP` module allows for the uniform use and manipulation of different SSP libraries, enabling seamless ingestion of models and data from various sources in the literature.
-The current version includes interfaces to a range of SSP models, including:
-- PopStar [@molla+09]
-- Bruzual and Charlot (BC03) [@bc+03]
-- E-MILES [@vazdekis+16]
-- X-Shooter Spectral Library (XSL) [@verro+22] SSP models.
+First, the SSP module enables the consistent use and manipulation of different SSP libraries. This allows for the seamless ingestion of models and data from various literature sources.
+The current version includes interfaces to a range of SSP models, including PopStar [@molla+09], Bruzual and Charlot (BC03) [@bc+03], E-MILES [@vazdekis+16], and the X-Shooter Spectral Library (XSL) [@verro+22] SSP models.
 
-For any SSP model integrated into PST, the library provides tools for interpolating across stellar ages, metallicities, and wavelengths. Users can easily compute key quantities of the SSP, such as the stellar-mass-to-light ratio in a given band, colours, line indices, etc.
+For any SSP model integrated into PST, the library provides tools for interpolating across stellar ages, metallicities, and wavelengths. Users can easily compute key SSP quantities, such as the stellar mass-to-light ratio in a given band, colors, and line indices.
 
-Second, the `ChemicalEvolutionModel` classes represent the star formation and chemical enrichment histories required to produce composite spectral energy distributions and additional derived quantities. They implement several widely-used analytic prescriptions for modeling SFHs, such as exponentially declining or log-normal models, as well as complex SFH representations, such as table-based SFHs and particle-like data models, particularly suitable for post-processing the results from cosmological hydrodynamical simulations.
+Second, the `ChemicalEvolutionModel` classes represent the star formation and chemical enrichment histories required to produce composite spectral energy distributions and additional derived quantities. These classes implement several widely used analytic prescriptions for modeling star formation histories (SFHs), such as exponentially declining or log-normal models. They also implement complex SFH representations, such as table-based SFHs and particle-like data models. These models are particularly suitable for post-processing results from cosmological hydrodynamic simulations.
 
-Third, PST features a dedicated `observables` module to predict additional quantities from spectra, such as broadband photometric fluxes, colours, and equivalent widths, which are useful for estimating the strength of absorption or emission lines. PST includes automatic integration with the photometric filters provided by the [Spanish Virtual Observatory Filter Profile Service](http://svo2.cab.inta-csic.es/theory/fps/) [@rodrigo+20] for synthetic photometry calculations, as well as popular line indices such as the Lick system [@worthey+94].
+Third, PST features a dedicated `observables` module that can predict additional quantities from spectra, such as broadband photometric fluxes, colours, and equivalent widths, which are useful for estimating the strength of absorption or emission lines. PST includes automatic integration with the photometric filters provided by the [Spanish Virtual Observatory Filter Profile Service](http://svo2.cab.inta-csic.es/theory/fps/) [@rodrigo+20] for synthetic photometry calculations, as well as popular line indices such as the Lick system [@worthey+94].
 
 # Documentation and tutorials
 
-To facilitate the use of PST, we provide a comprehensive set of tutorials in the form of Jupyter notebooks. These tutorials cover the following topics:
+To make PST easier to use, we provide a set of comprehensive tutorials in the form of Jupyter notebooks. These tutorials cover the following topics:
 
 - Interacting with SSP models and exploring their fundamental properties.
 - Producing composite spectra using:
-  - Analytic SFH models.
-  - Table-based SFH models.
+  - Analytic SFH models;
+  - Table-based SFH models;
   - Particle-like data representations.
 - Predicting observable quantities for a grid of models.