StarEstate: A Python Package for Galactic Population Synthesis

TL;DR

The software combines statistical generation with stellar physics by mapping synthetic populations to MESA or rapid SSE/BSE evolutionary tracks, and allows the user to predict spatial distributions of diverse stellar objects, providing a flexible resource for interpreting galactic surveys.

Abstract

I present StarEstate, an open-source Python package for producing rapid, statistically robust galactic population synthesis models. By utilizing optimized pre-calculated inverse-cumulative distribution function samplers, the tool generates synthetic populations from pre-generated grids of stellar tracks orders of magnitude faster than traditional numerical integration methods. A key morphological feature is the probabilistic assignment of stars to spiral arms based on age-dependent dynamical temperature, reproducing the observation that young tracers tightly confine to arms while older populations disperse. The software combines statistical generation with stellar physics by mapping synthetic populations to MESA or rapid SSE/BSE evolutionary tracks. Users can inspect specific evolutionary stages through automatic hierarchical classification, distinguishing evolutionary phases and spectral classes like Wolf-Rayet, O-type, or red supergiant stars across different metallicity environments. StarEstate's features allow the user to predict spatial distributions of diverse stellar objects, providing a flexible resource for interpreting galactic surveys.