Introducing piXedfit: A Spectral Energy Distribution Fitting Code Designed for Resolved Sources

Abstract

We present piXedfit, pixelized spectral energy distribution (SED) fitting, a Python package that provides tools for analyzing spatially resolved properties of galaxies using multiband imaging data alone or in combination with integral field spectroscopy (IFS) data. It has six modules that can handle all tasks in the spatially resolved SED fitting. The SED-fitting module uses the Bayesian inference technique with two kinds of posterior sampling methods: Markov Chain Monte Carlo (MCMC) and random dense sampling of parameter space (RDSPS). We test the performance of the SED-fitting module using mock SEDs of simulated galaxies from IllustrisTNG. The SED fitting with both posterior sampling methods can recover physical properties and star formation histories of the IllustrisTNG galaxies well. We further test the performance of piXedfit modules by analyzing 20 galaxies observed by the CALIFA and MaNGA surveys. The data are comprised of 12-band imaging data from the Galaxy Evolution Explorer, SDSS, 2MASS, and WISE and the IFS data from CALIFA or MaNGA. The piXedfit package can spatially match (in resolution and sampling) the imaging and IFS data. By fitting only the photometric SEDs, piXedfit can predict the spectral continuum, Dn 4000, H α , and H β well. The star formation rate derived by piXedfit is consistent with that derived from H α emission. The RDSPS method gives equally good fitting results as the MCMC and is much faster. As a versatile tool, piXedfit is equipped with a parallel computing module for efficient analysis of large data sets and will be made publicly available (https://github.com/aabdurrouf/piXedfit).

Toward Precise Galaxy Evolution: A Comparison between Spectral Indices of z ∼1 Galaxies in the IllustrisTNG Simulation and the LEGA-C Survey

“…Alternatively, the stellar masses could be derived by fitting mock SEDs to mimic observations. Previous works found small systematic offsets (overall < 0.1 dex) between the stellar masses from SED modeling and directly summing up stellar particles (Lower et al 2020;Abdurro'uf et al 2021), and we use the latter for simplicity.…”

“…Figures 6 and 7 encourage further development in stellar evolution and population synthesis models to improve the accuracy of conversion between observables and physical quantities (ages and metallicities) of stars and galaxies. Full-spectral fitting algorithms have improved significantly and now show great successes (e.g., Leja et al 2017;Carnall et al 2018;Robotham et al 2020;Abdurro'uf et al 2021). When the data quality is improved, e.g., the typical uncertainties of the LEGA-C sample are ∼0.03 for D n 4000 and ∼0.4 for EW(Hδ), the differences among models become the dominant source of uncertainties, limiting the accuracy of the recovered star-formation histories of galaxies.…”

The eROSITA Final Equatorial-Depth Survey (eFEDS)

“…The largest body of spectroscopic redshift information comes from the SDSS survey (York et al 2000;Gunn et al 2006;Smee et al 2013;Abdurro'uf et al 2022), totalling more than 68k spectra of 61k science targets within the outer bounds of the eFEDS field. We collected archival public data from SDSS phases I-IV (Ahumada et al 2020), as well as the results of the recent dedicated SPIDERS (Spectroscopic identifications of eROSITA sources) campaign (Comparat et al 2020, Merloni et al, in prep.…”

APOGEE-2S Mg–Al anti-correlation of the metal-poor globular cluster NGC 2298

“…DR 17 (Abdurro'uf et al 2022) is the final release of the APOGEE-2 survey from SDSS-IV. It includes all APOGEE-2 data taken at APO through November 2020 and at LCO through January 2021.…”