resolve conflicts paranoya patch 1

paper/paper.bib

@@ -70,30 +70,3 @@ @ARTICLE{verro+22
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }
 
-@ARTICLE{tng,
-       author = {{Nelson}, Dylan and {Springel}, Volker and {Pillepich}, Annalisa and {Rodriguez-Gomez}, Vicente and {Torrey}, Paul and {Genel}, Shy and {Vogelsberger}, Mark and {Pakmor}, Ruediger and {Marinacci}, Federico and {Weinberger}, Rainer and {Kelley}, Luke and {Lovell}, Mark and {Diemer}, Benedikt and {Hernquist}, Lars},
-        title = "{The IllustrisTNG simulations: public data release}",
-      journal = {Computational Astrophysics and Cosmology},
-     keywords = {Methods: data analysis, Methods: numerical, Galaxies: formation, Galaxies: evolution, Data management systems, Data access methods, Distributed architectures, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
-         year = 2019,
-        month = may,
-       volume = {6},
-       number = {1},
-          eid = {2},
-        pages = {2},
-          doi = {10.1186/s40668-019-0028-x},
-archivePrefix = {arXiv},
-       eprint = {1812.05609},
- primaryClass = {astro-ph.GA},
-       adsurl = {https://ui.adsabs.harvard.edu/abs/2019ComAC...6....2N},
-      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
-}
-
-@ARTICLE{Q1_quenching,
-       author = {{Corcho-Caballero}, Pablo and {et al.}},
-        title = "{Euclid. A probabilistic classification of quenched galaxies}",
-      journal = {\aap},
-     keywords = {galaxies: general, evolution, fundamental parameters, star formation, stellar content},
-         year = {in preparation},
-}
-

paper/paper.md

@@ -44,32 +44,36 @@ bibliography: paper.bib
 
 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.
 
-PST is a Python library that offers a comprehensive and flexible framework for stellar population synthesis. It supports the synthesis of composite spectra using multiple SSP libraries, incorporates additional effects such as cosmic redshift and dust extinction, and computes observable quantities, including broadband photometric fluxes and equivalent widths, with ease and efficiency.
+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 it computes several observable quantities derived from the spectra, including broadband photometric fluxes and equivalent widths.
 
 # Statement of need
 
 Compared to alternative approaches in the literature, the user-friendly modular framework of PST is conceived 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 model both photometric and spectroscopic data.
+- To enable the simultaneous and self-consistent analysis of photometric and spectroscopic data from different instruments.
+
+PST is currently a dependency of [PyKOALA](https://github.com/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://github.com/PabloCorcho/pst-hbsps)), where it is coupled with the [Cosmosis](https://cosmosis.readthedocs.io/en/latest/) Monte Carlo sampling framework to infer the physical properties of galaxies from the observed colours and spectra.
 
 # Features and functionality
 
-PST design is built around three core 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 Charlote 03 [@bc+03]
+- Bruzual and Charlot (BC03) [@bc+03]
 - E-MILES [@vazdekis+16]
-- 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 SSP models, such as the stellar-mass-to-light ratio in a given band, colours, line indices, etc.
+- 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.
 
-Second, the `ChemicalEvolutionModel` classes represent the star formation and chemical enrichment histories required to produce 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. 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.
 
-Third, PST features a dedicated `observables` module to predict additional quantities from spectra, such as broadband photometric fluxes and equivalent widths. PST includes automatic integration with the photometric filters provided by the [Spanish Virtual Observatory Filter Profile Servive](http://svo2.cab.inta-csic.es/theory/fps/) for synthetic photometry calculations, as well as popular line indices such as the Lick system.
+Third, PST features a dedicated `observables` module to predict additional quantities from spectra, such as broadband photometric fluxes, colours, and equivalent widths. PST includes automatic integration with the photometric filters provided by the [Spanish Virtual Observatory Filter Profile Servive](http://svo2.cab.inta-csic.es/theory/fps/) for synthetic photometry calculations, as well as popular line indices such as the Lick system.
 
-# Tutorials
+# 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:
 
@@ -80,8 +84,10 @@ To facilitate the use of PST, we provide a comprehensive set of tutorials in the
   - Particle-like data representations.
 - Predicting observable quantities for a grid of models.
 
+Full documentation is available [online](https://population-synthesis-toolkit.readthedocs.io/en/latest/).
+
 # Acknowledgements
 
-?
+We acknowledge financial support from the Spanish State Research Agency (AEI/10.13039/501100011033) through grant PID2019-107408GB-C42.
 
 # References