Benjamin Roulston

Research Overview

My research focuses on the evolution of stars in close binary systems and the observational signatures of chemically peculiar stars, combining physical modeling, statistical inference, and large astronomical datasets.

Research Overview

I develop physically motivated, data-driven models to connect observations of stellar populations to the underlying processes that govern binary evolution. My work emphasizes forward modeling, uncertainty quantification, and reproducible computational workflows.

Research Themes

Close Binary Star Evolution

Common-envelope evolution, mass transfer, binary population modeling, and connections between theory and observed stellar populations.

Chemically Peculiar & Dwarf Carbon Stars

Formation channels, binary origins, and observational constraints on chemically peculiar stellar systems.

Stellar Evolution Modeling

Detailed stellar evolution calculations using MESA, including parameter studies and model validation against observations.

Statistical & Computational Methods

Bayesian inference, MCMC, hierarchical modeling, and uncertainty quantification for high-dimensional astrophysical problems.

Active & Recent Projects

Modeling the Formation of Dwarf Carbon Stars

Description of the project, scientific motivation, and key results or goals.

Binary Population Modeling & Survey Constraints

Forward modeling of binary populations constrained by survey data, with emphasis on selection effects and uncertainties.

Methods & Tools

Modeling

  • MESA stellar evolution
  • Forward population modeling
  • Physical parameter estimation

Statistics

  • Bayesian inference
  • MCMC & hierarchical models
  • Uncertainty quantification

Computation

  • HPC & cluster computing
  • Python, Julia, Fortran
  • Reproducible workflows

Publications

Selected publications related to binary star evolution, chemically peculiar stars, and stellar modeling.

Full publication list →

Students & Collaboration

I work closely with undergraduate researchers on computational and observational projects and actively collaborate with colleagues across institutions on survey, instrumentation, and modeling efforts.