Deep in the cosmic web, ancient sound waves from the early universe have left their fingerprints as Baryon Acoustic Oscillations (BAO) - cosmic rulers that help us measure the expansion of space itself. But detecting these subtle patterns requires analyzing the Lyman-alpha forest, those absorption lines created when distant quasar light passes through intervening hydrogen clouds. This is where the challenge begins: how do you extract precise cosmological measurements from this complex three-dimensional data?
Vega rises to this challenge as a specialized Python toolkit that computes 3D correlation function models for Lyman-alpha forest tracers and fits observational data to reveal BAO signatures. Built to work seamlessly with the picca pipeline, it handles the heavy lifting of modeling quasar positions, metal line absorption, and flux correlations across vast cosmic distances. The tool integrates multiple sampling methods including Polychord and PocoMC for robust parameter estimation, while leveraging MPI parallelization for handling the massive datasets that modern sky surveys produce.
Currently powering research that pushes the boundaries of precision cosmology, Vega has contributed to major studies measuring cosmic expansion rates and testing our understanding of dark energy. Its modular design makes it accessible to researchers working with large spectroscopic surveys like BOSS and eBOSS, while its robust statistical framework ensures that the whispers of ancient cosmic sound waves can be heard above the noise of observational data.
⭐ Stars: 11
💻 Language: Python
🔗 Repository: andreicuceu/vega