Every star tells a story written in light, but deciphering that stellar language requires comparing observed spectra against theoretical models. Gollum bridges this gap by providing seamless programmatic access to precomputed synthetic spectral model grids - the astronomical equivalent of having a universal translator for starlight. Whether you’re characterizing brown dwarfs, analyzing exoplanet host stars, or studying stellar atmospheres, this microservice transforms the traditionally cumbersome process of model access into elegant Python calls.
Built on the robust foundation of astropy’s specutils, Gollum currently supports major model grids including PHOENIX and Sonora-Bobcat, offering researchers a unified API to explore parameter spaces spanning effective temperature, surface gravity, and metallicity. The toolkit includes an interactive dashboard where astronomers can manipulate sliders to adjust intrinsic stellar properties and extrinsic observational effects like rotational broadening and radial velocity shifts, watching in real-time as synthetic spectra morph to match their data. This visual approach democratizes model fitting, making complex atmospheric modeling accessible through intuitive human-in-the-loop interactions.
With its clean API design inspired by lightkurve and potential future integration with the Starfish framework, Gollum represents a new paradigm in computational astronomy - where the vast libraries of theoretical stellar models become as accessible as any Python package. From graduate students fitting their first spectra to seasoned researchers conducting large-scale surveys, this microservice is poised to accelerate discovery by removing the technical barriers between astronomers and the synthetic models that unlock stellar secrets.
⭐ Stars: 24
💻 Language: Python
🔗 Repository: BrownDwarf/gollum