Welcome to Vega’s documentation!#
Vega is a tool for computing 3D correlation function and power spectrum models primarily for Lyman-α (Lyα) forest analyses. It is built to be modular and highly flexible in terms of the tracers being used. So far, Vega has been used to analyze the Lyα forest auto-correlation, its cross-correlation with galaxies and quasars (e.g., Gerardi et al. 2022, Gordon et al. 2023, Herrera-Alcantar et al. 2025, Karaçaylı et al. 2026), as well as auto- and cross-correlations of metal lines such as CIV and SiIV (e.g., Guy et al. 2025, Bault et al. 2026), Damped Lyman-α (DLA) absorbers (Pérez-Ràfols et al. 2023), and Strong Blended Lyman-α (SBLA) absorbers (Pérez-Ràfols et al. 2023).
Vega is currently being used by the Lyα forest working group in DESI to measure Baryon Acoustic Oscillations (BAO) and perform full-shape analyses of Lyα forest auto- and cross-correlations (e.g., DESI et al. 2025a, DESI et al. 2025b, Cuceu et al. 2025).
Free software: GPL-3.0-or-later License
Documentation: https://vega.readthedocs.io.
Referencing: If you use Vega in a publication, please give the link to this repository (andreicuceu/vega). The best descriptions of what the code does are found in Cuceu et al. (2022) and Cuceu et al. (2025).
There are several tutorials and examples available to help you get started with Vega:
Tutorials & Examples:
Vega modules are documented here:
- Vega Modules
VegaInterfaceBuildConfigFitResultsCorrelationItemDataModelCorrelationFunctionPowerSpectrumScaleParametersCoordinatesPktoXiMetalsBroadbandPolynomialsAnalysisMinimizerOutputVegaArinyoErrorVegaBoundsErrorVegaModelErrorapply_blinding()bias_beta()compute_gauss_smoothing()compute_kn_smoothing()compute_log_cov_det()compute_masked_invcov()convert_instance_to_dictionary()find_file()get_growth_interp()get_legendre_bins()growth_function()growth_integrand()hubble()normalized_growth_factor()percival_correction()sinc()VegaPlotsShellWedgeRtWedgearray_or_dict()plot_wedges()SamplerPolychordbuild_names()get_default_values()get_latex()
Other stuff: