GalaPy
Role: lead author & maintainer. Stack: C++ compute core exposed through a Python API; hybrid parallelisation (vectorised array programming, shared-memory concurrency, distributed-memory message passing).
GalaPy simulates the panchromatic emission of galaxies from X-rays to radio and performs Bayesian spectral-energy-distribution fitting. Models are generated on the fly rather than interpolated from a template library, which is what allows a peak throughput of almost 1000 SEDs per second on a single core — the fastest SED-generation tool of its kind.
The library is designed around an object-oriented architecture that keeps it portable with minimal memory overhead, and it scales from a laptop to an HPC allocation without changing the user-facing API. Recent additions include parallel sampling for Bayesian inference and a fully analytical, panchromatic AGN component.
Since release, GalaPy has been adopted by groups across Europe, Asia and South America, for work ranging from nearby resolved galaxies to JWST high-redshift candidates. Its role in my wider programme is described in my research on galaxy formation and evolution.
Presented in Astronomy & Astrophysics 685, A161 (2024) — see the library paper — with the computational design detailed in Astronomy and Computing 55, 101079 (2026): the implementation paper.
