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diff --git a/papers/joss/paper.md b/papers/joss/paper.md
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--- a/papers/joss/paper.md
+++ b/papers/joss/paper.md
@@ -1,5 +1,5 @@
---
-title: '[binary\_c-python]{.smallcaps}: A python-based stellar population synthesis tool and interface to [binary\_c]{.smallcaps}'
+title: '[binary_c-python]{.smallcaps}: A python-based stellar population synthesis tool and interface to [binary_c]{.smallcaps}'
tags:
- Python
- astronomy
@@ -13,123 +13,38 @@ authors:
affiliations:
- name: Department of Physics, University of Surrey, Guildford, GU2 7XH, Surrey, UK
index: 1
-date: 18 June 2022
+date: 28 July 2022
bibliography: paper.bib
---
-Summary {#sec:summary}
-=======
-
-We present our package
-[[binary\_c-python]{.smallcaps}](https://ri0005.pages.surrey.ac.uk/binary_c-python/),
-which is aimed to provide a convenient and easy-to-use interface to the
-[[binary\_c]{.smallcaps}](http://personal.ph.surrey.ac.uk/~ri0005/doc/binary_c/binary_c.html)Â [@izzardNewSyntheticModel2004; @izzardPopulationNucleosynthesisSingle2006; @izzardPopulationSynthesisBinary2009; @izzardBinaryStarsGalactic2018]
-framework, allowing the user to rapidly evolve individual systems and
-populations of stars. [binary\_c-python]{.smallcaps}Â is available on
-[Pip](https://pypi.org/project/binarycpython/) and on
-[Gitlab](https://gitlab.com/binary_c/binary_c-python).
-
-The user can control output from [binary\_c]{.smallcaps}Â by providing
-[binary\_c-python]{.smallcaps}Â with logging statements that are
-dynamically compiled and loaded into [binary\_c]{.smallcaps}.
-[binary\_c-python]{.smallcaps}Â uses multiprocessing to utilise all the
-cores on a particular machine, and can run populations with HPC cluster
-workload managers like [HTCondor]{.smallcaps} and [Slurm]{.smallcaps},
-allowing the user to run simulations on very large computing clusters.
-
-[binary\_c-python]{.smallcaps}Â is easily interfaced or integrated with
-other Python-based codes and libraries, e.g. sampling codes like
-[Emcee]{.smallcaps} or [Dynesty]{.smallcaps}, or the astrophysics
-oriented package
-[Astropy]{.smallcaps}Â [@astropycollaborationAstropyCommunityPython2013; @foreman-mackeyEmceeMCMCHammer2013; @astropycollaborationAstropyProjectBuilding2018; @speagleDynestyDynamicNested2020].
-Moreover, it is possible to provide custom system-generating functions
-through our function hooks, allowing third-party packages to manage the
-properties of the stars in the populations and evolve them through
-[binary\_c-python]{.smallcaps}.
-
-Recent developments in [binary\_c]{.smallcaps}Â include standardised
-output datasets called *ensembles*.
-[binary\_c-python]{.smallcaps}Â easily processes these datasets and
-provides a suite of utility functions to handle them. Furthermore,
-[binary\_c]{.smallcaps}Â now includes the *ensemble-manager* class, which
-makes use of the core functions and classes of
-[binary\_c-python]{.smallcaps}Â to evolve a grid of stellar populations
-with varying input physics, allowing for large, automated parameter
-studies through a single interface.
-
-We provide
-[documentation](https://ri0005.pages.surrey.ac.uk/binary_c-python/index.html)
-that is automatically generated based on docstrings and a suite of
-[Jupyter]{.smallcaps}
-[notebooks](https://ri0005.pages.surrey.ac.uk/binary_c-python/example_notebooks.html).
-These notebooks consist of technical tutorials on how to use
-[binary\_c-python]{.smallcaps}, and use-case scenarios aimed at doing
-science. Much of [binary\_c-python]{.smallcaps}Â is covered by unit tests
-to ensure reliability and correctness, and the test coverage is
-continually increased as the package is being improved.
-
-Statement of need {#sec:statement}
-=================
-
-In the current scientific climate [Python]{.smallcaps} is ubiquitous,
-and while lower-level codes written in, e.g., [Fortran]{.smallcaps} or
-[C]{.smallcaps} are still widely used, much of the newer software is
-written in [Python]{.smallcaps}, either entirely or as a wrapper around
-other codes and libraries. Education in programming also often includes
-[Python]{.smallcaps} courses because of its ease of use and its
-flexibility. Moreover, [Python]{.smallcaps} has a large community with
-many resources and tutorials. We have created
-[binary\_c-python]{.smallcaps}Â to allow students and scientists alike to
-explore current scientific issues while enjoying the familiar syntax,
-and at the same time make use of the plentiful scientific and
-astrophysical packages like [Numpy]{.smallcaps}, [Scipy]{.smallcaps},
-[Pandas]{.smallcaps}, [Astropy]{.smallcaps}Â and platforms like
-[Jupyter]{.smallcaps}.
-
-Earlier versions of [binary\_c-python]{.smallcaps}Â were written in Perl,
-where much of the logic and structure were developed and debugged. This
-made porting to [Python]{.smallcaps} relatively easy.
-
-Projects that use [binary\_c-python]{.smallcaps} {#sec:projects}
-================================================
-
-[binary\_c-python]{.smallcaps}Â has already been used in a variety of
-situations, ranging from pure research to educational purposes, as well
-as in outreach events. In the summer of 2021 we used
-[binary\_c-python]{.smallcaps}Â as the basis for the interactive classes
-on stellar ecosystems during the [International Max-Planck Research
-School summer school 2021 in
-Heidelberg](https://www2.mpia-hd.mpg.de/imprs-hd/SummerSchools/2021/),
-where students were introduced to the topic of population synthesis and
-were able to use our notebooks to perform their own calculations.
-[binary\_c-python]{.smallcaps}Â has been used in @mirouh_etal22, where
-improvements to tidal interactions between stars were implemented, and
-initial birth parameter distributions were varied to match to observed
-binary systems in star clusters. A Master's thesis project, aimed at
-finding the birth system parameters of the V106 stellar system,
-comparing observations to results of [binary\_c]{.smallcaps}Â and
-calculating the maximum likelihood with Bayesian inference through
-Markov chain Monte Carlo sampling. The project made use of
-[binary\_c-python]{.smallcaps}Â and the [Emcee]{.smallcaps} package.
-
-Currently [binary\_c-python]{.smallcaps}Â is used in several ongoing
-projects that study the effect of birth distributions on the occurrence
-of carbon-enhanced metal-poor (CEMP) stars, the occurrence and
-properties of accretion disks in main-sequence stars and the predicted
-observable black hole distribution by combining star formation and
-metallicity distributions with the output of [binary\_c]{.smallcaps}.
-Moreover, we use the *ensemble* output structure to generate datasets
-for galactic chemical evolution on cosmological timescales, where we
-rely heavily on the utilities of [binary\_c-python]{.smallcaps}.
-
-Acknowledgements {#sec:orgf6f5520}
-================
-
-We acknowledge the helpful discussions and early testing efforts from M.
-Delorme, G. Mirouh, and D. Tracey, and the early work of J. Andrews
-which inspired our Python-C interface code. DDH thanks the UKRI/UoS for
-the funding grant H120341A. RGI thanks STFC for funding grants
-[ST/R000603/1](https://gtr.ukri.org/projects?ref=ST%2FR000603%2F1) and
-[ST/L003910/2](https://gtr.ukri.org/projects?ref=ST/L003910/2).
-
-[^1]: E-mail: <dh00601@surrey.ac.uk>Â (DDH)
+# Summary
+
+We present our package [`binary_c-python`](https://ri0005.pages.surrey.ac.uk/binary_c-python/), which is aimed to provide a convenient and easy-to-use interface to the [`binary_c`](http://personal.ph.surrey.ac.uk/~ri0005/doc/binary_c/binary_c.html) [@izzardNewSyntheticModel2004; @izzardPopulationNucleosynthesisSingle2006; @izzardPopulationSynthesisBinary2009; @izzardBinaryStarsGalactic2018] framework, allowing the user to rapidly evolve individual systems and populations of stars. `binary_c-python` is available on [`Pip`](https://pypi.org/project/binarycpython/) and on [`Gitlab`](https://gitlab.com/binary_c/binary_c-python).
+
+The user can control output from `binary_c` by providing `binary_c-python` with logging statements that are dynamically compiled and loaded into `binary_c`. `binary_c-python` uses multiprocessing to utilise all the cores on a particular machine, and can run populations with HPC cluster workload managers like `HTCondor` and `Slurm`, allowing the user to run simulations on very large computing clusters.
+
+`binary_c-python` is easily interfaced or integrated with other Python-based codes and libraries, e.g. sampling codes like `Emcee` or `Dynesty`, or the astrophysics oriented package `Astropy` [@astropycollaborationAstropyCommunityPython2013; @foreman-mackeyEmceeMCMCHammer2013; @astropycollaborationAstropyProjectBuilding2018; @speagleDynestyDynamicNested2020]. Moreover, it is possible to provide custom system-generating functions through our function hooks, allowing third-party packages to manage the properties of the stars in the populations and evolve them through `binary_c-python`.
+
+Recent developments in `binary_c` include standardised output datasets called *ensembles*. `binary_c-python` easily processes these datasets and provides a suite of utility functions to handle them. Furthermore, `binary_c` now includes the *ensemble-manager* class, which makes use of the core functions and classes of `binary_c-python` to evolve a grid of stellar populations with varying input physics, allowing for large, automated parameter studies through a single interface.
+
+We provide [documentation](https://ri0005.pages.surrey.ac.uk/binary_c-python/index.html) that is automatically generated based on docstrings and a suite of `Jupyter` [notebooks](https://ri0005.pages.surrey.ac.uk/binary_c-python/example_notebooks.html). These notebooks consist of technical tutorials on how to use `binary_c-python`, and use-case scenarios aimed at doing science. Much of `binary_c-python` is covered by unit tests to ensure reliability and correctness, and the test coverage is continually increased as the package is being improved.
+
+# Statement of need
+
+In the current scientific climate `Python` is ubiquitous, and while lower-level codes written in, e.g., `Fortran` or `C` are still widely used, much of the newer software is written in `Python`, either entirely or as a wrapper around other codes and libraries. Education in programming also often includes `Python` courses because of its ease of use and its flexibility. Moreover, `Python` has a large community with many resources and tutorials. We have created `binary_c-python` to allow students and scientists alike to explore current scientific issues while enjoying the familiar syntax, and at the same time make use of the plentiful scientific and astrophysical packages like `Numpy`, `Scipy`, `Pandas`, `Astropy` and platforms like `Jupyter`.
+
+Earlier versions of `binary_c-python` were written in Perl, where much of the logic and structure were developed and debugged. This made porting to `Python` relatively easy.
+
+# Projects that use `binary_c-python`
+
+`binary_c-python` has already been used in a variety of situations, ranging from pure research to educational purposes, as well as in outreach events. In the summer of 2021 we used `binary_c-python` as the basis for the interactive classes on stellar ecosystems during the [International Max-Planck Research School summer school 2021 in Heidelberg](https://www2.mpia-hd.mpg.de/imprs-hd/SummerSchools/2021/), where students were introduced to the topic of population synthesis and were able to use our notebooks to perform their own calculations. `binary_c-python` has been used in @mirouh_etal22, where improvements to tidal interactions between stars were implemented, and initial birth parameter distributions were varied to match to observed binary systems in star clusters. A Master's thesis project, aimed at finding the birth system parameters of the V106 stellar system, comparing observations to results of `binary_c` and calculating the maximum likelihood with Bayesian inference through Markov chain Monte Carlo sampling. The project made use of `binary_c-python` and the `Emcee` package.
+
+Currently `binary_c-python` is used in several ongoing projects that study the effect of birth distributions on the occurrence of carbon-enhanced metal-poor (CEMP) stars, the occurrence and properties of accretion disks in main-sequence stars and the predicted observable black hole distribution by combining star formation and metallicity distributions with the output of `binary_c`. Moreover, we use the *ensemble* output structure to generate datasets for galactic chemical evolution on cosmological timescales, where we rely heavily on the utilities of `binary_c-python`.
+
+# Acknowledgements
+
+We acknowledge the helpful discussions and early testing efforts from M. Delorme, G. Mirouh, and D. Tracey, and the early work of J. Andrews which inspired our Python-C interface code. DDH thanks the UKRI/UoS for the funding grant H120341A. RGI thanks STFC for funding grants [ST/R000603/1](https://gtr.ukri.org/projects?ref=ST%2FR000603%2F1) and [ST/L003910/2](https://gtr.ukri.org/projects?ref=ST/L003910/2).
+
+# References
+
+[^1]: E-mail: <dh00601@surrey.ac.uk> (DDH)
diff --git a/papers/joss/paper.pdf b/papers/joss/paper.pdf
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