FAST-SBF: an automatic procedure for the measurement of Surface Brightness Fluctuations for large sky surveys

TL;DR

The paper introduces FAST-SBF, a Python-based, automated pipeline for measuring Surface Brightness Fluctuations to derive galaxy distances in next-generation wide-field surveys. It provides a end-to-end workflow including sky background handling, galaxy modelling, PSF selection, masking of contaminant sources, residual power correction, fluctuation magnitude and color measurement, uncertainty propagation, and a color-dependent distance calibration that links m̄ to Mī via a cubic relation. Validation on HSC-SSP and NGVS data shows excellent agreement with literature distances and confirms the method’s applicability to dwarf galaxies, enabling robust SBF distances over large samples. The work demonstrates FAST-SBF’s potential to support LSST, Euclid, and Roman in constraining the 3D structure of the local universe, while highlighting current limitations and the need for user training before public release.

Abstract

The Surface Brightness Fluctuation method is one of the most reliable and efficient ways of measuring distances to galaxies within 100 Mpc. 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 for accurate SBF measurements across large samples of galaxies. With the upcoming next generation 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. 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. We validate 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 are also compared with recent results from the Next Generation Virgo Cluster Survey (NGVS) and with the SPoT stellar population synthesis models. 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 ($\approx$ minutes) and significantly reduces the need for user intervention. reduces at minimum the 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.

Figures (8)

• Figure 1: Spatial distribution of the galaxies in the three fields we studied. The color represents their velocity in respect to the CMB frame. Small circles represent dwarf galaxies, while bright galaxies are represented with bigger size and are labeled. The box size is $1.3\times1$ deg and is the same for all the panels.
• Figure 2: Flow chart of the main process of the FAST-SBF pipeline.
• Figure 3: Example of the procedure to estimate the $i$-band background of NGC 5831. The black solid line shows the radial profile of the flux in the input image, azimuthally averaged in circular bins of 50 pixels. The shaded gray area is the region where the background is estimated. The red dashed line represents the background estimated with the method described in the text.
• Figure 4: SBF analysis images and plots for NGC 5813. Top row: $i$-band image, residual, and residual masked image (left to right). The black circles in the third panel show the inner and outer radii of the annulus adopted for the SBF measurements. Middle row: example of the PSF selection process. In the left panel are shown the stars pre-selected based on photometric criteria. Stars highlighted with red borders represent a sub-sample of those subsequently chosen for the fitting. In the right panel, the radial profile of the full sample of PSF selected is shown. The red line shows the median profile, while thin gray lines are azimuthal averaged profiles of single PSFs. The PSFs are normalized to have a total energy of one. Bottom left panel: fitted luminosity function of external sources. The green circles represent the binned observational data. The solid green curve is the best fit to the data. The two components of the luminosity function, the background galaxies and GCLF, are shown with a blue dotted and red dashed curves, respectively. Bottom middle panel: the azimuthal average of the residual image power spectrum using one of the good PSFs. The gray dots represent the observational data. The solid blue curve is the fit obtained according to the procedure described in the text. The red dashed lines represent the range of wavenumbers used for the fit. The green dashed curve shows the PSF power spectrum. The green dotted horizontal line represents the fitted white noise value $P_1$. Bottom right panel: estimated $P_\mathrm{0}$ (grey dots) at different $k_{\mathrm{start}}$; the vertical black dotted lines indicate the most stable region in which $P_\mathrm{0}$ is calculated, while the horizontal one is the final value of $P_0$ for the specific PSF.
• Figure 5: Comparison with the measurements based on NGVS data. The left panels show the SBF magnitude (upper panel) and distance modulus (lower panel) differences between our measurements and Cantiello2024, as a function of the $(g{-}i)$ color. The right panels show the same comparison but as a function of the galaxy total $i$-band magnitude.