TL;DR
dart_board is a new code that combines rapid binary evolution with Markov chain Monte Carlo methods, enabling efficient, flexible, and uncertainty-aware binary population synthesis for various stellar systems.
Contribution
We introduce dart_board, a publicly available code that integrates MCMC with binary evolution models, allowing for flexible modeling of populations and individual systems with observational uncertainties.
Findings
Successfully tested with mock systems
Applied to HMXB populations in LMC
Included observational data for specific systems
Abstract
By employing Monte Carlo random sampling, traditional binary population synthesis (BPS) offers a substantial improvement in efficiency over brute force, grid-based studies. Even so, BPS models typically require a large number of simulation realizations, a computationally expensive endeavor, to generate statistically robust results. Recent advances in statistical methods have led us to revisit the traditional approach to BPS. In this work we describe our publicly available code dart_board which combines rapid binary evolution codes, typically used in traditional BPS, with modern Markov chain Monte Carlo methods. dart_board takes a novel approach that treats the initial binary parameters and the supernova kick vector as model parameters. This formulation has several advantages, including the ability to model either populations of systems or individual binaries, the natural inclusion of…
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