FAST-SBF: An automatic procedure for the measurement of surface brightness fluctuations for large sky surveys

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Abstract
Context. The surface brightness fluctuation (SBF) method is one of the most reliable and efficient ways of measuring distances to galaxies within 100 Mpc, a matter of crucial importance for all fields of astrophysics. While recent implementations have increasingly relied on space-based observations, SBF remains effective when applied to ground-based data. In particular, deep, wide-field imaging surveys with sub-arcsecond seeing conditions allows us to make accurate SBF measurements across large samples of galaxies. With the upcoming next generation of wide-area imaging surveys, the thousands of galaxies suitable for SBF measurements will give us the opportunity to constrain the 3D structure of the local Universe. Aims. We present FAST-SBF, a new Python-based pipeline for measuring SBF, developed to support the analysis of large datasets from upcoming wide-field imaging surveys such as LSST, Euclid , and Roman . The procedure, still in the testing and development stage, is designed for automation and minimal user intervention, offering a fast and flexible approach to SBF distance estimation. Methods. We validated the performance of the procedure on high-quality imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), a precursor to LSST, analyzing a sample of both luminous early-type galaxies and fainter dwarfs. Our measurements were also compared with recent results from the Next Generation Virgo Cluster Survey (NGVS) and with the SPoT stellar population synthesis models. Results. The results show excellent agreement with published distances, with the capability of measuring the SBF signal also for faint dwarf galaxies. The pipeline allows the user to completely analyze a galaxy in relatively short time (≈ minutes) and significantly reduces the need for user intervention. The FAST-SBF tool is planned for public release to support the community in using SBF as a distance indicator in next-generation surveys.

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