Related papers: dart_board: Binary Population Synthesis with Marko…

Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observed properties. Binary population synthesis with full stellar-structure and evolution simulations are computationally…

It has become increasingly easy nowadays to collect approximate posterior samples via fast algorithms such as variational Bayes, but concerns exist about the estimation accuracy. It is tempting to build solutions that exploit approximate…

Many aspects of the evolution of stars, and in particular the evolution of binary stars, remain beyond our ability to model them in detail. Instead, we rely on observations to guide our often phenomenological models and pin down uncertain…

We study the evolution of binary stars in globular clusters using a new Monte Carlo approach combining a population synthesis code (StarTrack), and a simple treatment of dynamical interactions in the dense cluster core using a new tool for…

Dynamic Bayesian predictive synthesis is a formal approach to coherently synthesizing multiple predictive distributions into a single distribution. In sequential analysis, the computation of the synthesized predictive distribution has…

In this paper we present a brief overview of population synthesis methods with a discussion of their main advantages and disadvantages. In the second part, we present some recent results from synthesis models of close binary compact objects…

Galactic binary neutron stars (BNSs) are a unique laboratory to probe the evolution of BNSs and their progenitors. Here, we use a new version of the population synthesis code SEVN to evolve the population of Galactic BNSs, by modeling the…

We introduce the Hamming Ball Sampler, a novel Markov Chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction…

Mixture models provide a flexible representation of heterogeneity in a finite number of latent classes. From the Bayesian point of view, Markov Chain Monte Carlo methods provide a way to draw inferences from these models. In particular,…

Most massive stars are members of a binary or a higher-order stellar systems, where the presence of a binary companion can decisively alter their evolution via binary interactions. Interacting binaries are also important astrophysical…

Bayesian inference for Markov jump processes (MJPs) where available observations relate to either system states or jumps typically relies on data-augmentation Markov Chain Monte Carlo. State-of-the-art developments involve representing MJP…

Binary population synthesis calculations and associated predictions, especially event rates, are known to depend on a significant number of input model parameters with different degrees of sensitivity. At the same time, for systems with…

In this article we propose a novel MCMC method based on deterministic transformations T: X x D --> X where X is the state-space and D is some set which may or may not be a subset of X. We refer to our new methodology as Transformation-based…

We show that evolutionary computation can be implemented as standard Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms' can be constructed that are reversible Markov chains that satisfy detailed balance; it…

We now know that a large number of stars are born in multiple systems. Additionally, more than 70% of massive stars are found in close binary systems, meaning that they will interact over the course of their lifetime. This has strong…

We describe the public release of the Cluster Monte Carlo Code (CMC) a parallel, star-by-star $N$-body code for modeling dense star clusters. CMC treats collisional stellar dynamics using H\'enon's method, where the cumulative effect of…

Estimating the log-likelihood gradient with respect to the parameters of a Restricted Boltzmann Machine (RBM) typically requires sampling using Markov Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov chains are only…

Binary population synthesis (BPS) employs prescriptions to predict final fates, explosion or implosion, and remnant masses based on one or two stellar parameters at the evolutionary cutoff imposed by the code, usually at or near central…

The first version of the Binary Population Synthesizer (BiPoS1) is made publicly available. It allows to efficiently calculate binary distribution functions after the dynamical processing of a realistic population of binary stars during the…

Using our new Binary Population and Spectral Synthesis (BPASS) code we explore the affect of binary populations on the integrated spectra of galaxies. We also explore the interplay of binary populations and a varying maximum stellar mass.…