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}
@article{izzardCircumbinaryDiscsStellar2022,
title={Circumbinary Discs for Stellar Population Models},
author={Izzard, Robert G. and Jermyn, Adam S.},
year={2022},
month=oct,
journal={Monthly Notices of the Royal Astronomical Society},
issn={0035-8711},
doi={10.1093/mnras/stac2899},
urldate={2022-12-04},
abstract={We develop a rapid algorithm for the evolution of stable, circular, circumbinary discs suitable for parameter estimation and population synthesis modelling. Our model includes disc mass and angular momentum changes, accretion on to the binary stars, and binary orbital eccentricity pumping. We fit our model to the post-asymptotic giant branch (post-AGB) circumbinary disc around IRAS 08544-4431 finding reasonable agreement despite the simplicity of our model. Our best-fit disc has a mass of about 0.01 M{$\odot$} and angular momentum 2.7 \texttimes{} 1052 g cm2 s-1 {$\simeq$} 9M{$\odot$} km s-1 au, corresponding to 0.0079 and 0.16 of the common-envelope mass and angular momentum respectively. The best-fit disc viscosity is {$\alpha$}disc = 5 \texttimes{} 10-3 and our tidal torque algorithm can be constrained such that the inner edge of the disc Rin \textasciitilde{} 2a. The inner binary eccentricity reaches about 0.13 in our best-fitting model of IRAS 08544-4431, short of the observed 0.22. The circumbinary disc evaporates quickly when the post-AGB star reaches a temperature of \textasciitilde 6 \texttimes{} 104 K, suggesting that planetismals must form in the disc in about 104 yr if secondary planet formation is to occur, while accretion from the disc on to the stars at \textasciitilde 10 times the inner-edge viscous rate can double the disc lifetime.},
We present our package [`binary_c-python`](https://binary_c.gitlab.io/binary_c-python/), which is aimed to provide a convenient and easy-to-use interface to the [`binary_c`](https://binary_c.gitlab.io/binary_c)[@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://binary_c.gitlab.io/binary_c-python/).
We present our package [`binary_c-python`](https://binary_c.gitlab.io/binary_c-python/), which is aimed to provide a convenient and easy-to-use interface to the [`binary_c`](https://binary_c.gitlab.io/binary_c)[@izzardNewSyntheticModel2004;@izzardPopulationNucleosynthesisSingle2006;@izzardPopulationSynthesisBinary2009;@izzardBinaryStarsGalactic2018;izzardCircumbinaryDiscsStellar2022] 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://binary_c.gitlab.io/binary_c-python/).
`binary_c-python` contains many useful features that allow controlling and processing the output of `binary_c`. 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`. Moreover, we have recently added standardised output of events like Roche-lobe overflow or double compact-object formation to `binary_c`, and automatic parsing and managing of that output in `binary_c-python`. `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 large computing clusters.
@@ -41,7 +41,7 @@ The previous interface to `binary_c`, `binary_grid` was written in `Perl`, where
`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/). 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, implementing improvements to tidal interactions between stars and varying initial birth parameter distributions to match to observed binary systems in star clusters. The `binary_c-python` and `Emcee` packages were used in a Master's thesis project to find the birth system parameters of the V106 stellar system, compare observations to results of `binary_c`, and perform uncertainty with Bayesian uncertainty inference through Markov chain Monte Carlo sampling.
Currently, `binary_c-python` is used in several ongoing projects. These include a study on the effect of birth distributions on the occurrence of carbon-enhanced metal-poor (CEMP) stars, the abundance 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`. We also use the *ensemble* output structure to generate datasets for galactochemical evolution over cosmological timescales, where we rely heavily on the utilities of `binary_c-python`.
Currently, `binary_c-python` is used in several ongoing projects. These include a study on the effect of birth distributions on the occurrence of carbon-enhanced metal-poor (CEMP) stars, the abundance 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`. We also use the *ensemble* output structure to generate datasets for galactochemical evolution over cosmological timescales, where we rely heavily on the utilities of `binary_c-python` [@yates_etal2023].
