Introducing \texttt{piXedfit} -- a Spectral Energy Distribution Fitting Code Designed for Resolved Sources

We present \verb|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. \verb|piXedfit| 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 posteriors sampling methods: Markov Chain Monte Carlo (MCMC) and random densely-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 posteriors sampling methods can recover physical properties and star formation histories of the IllustrisTNG galaxies well. We further test the performance of \verb|piXedfit| modules by analyzing 20 galaxies observed by the CALIFA and MaNGA surveys. The data comprises of 12-band imaging data from GALEX, SDSS, 2MASS, and WISE, and the IFS data from CALIFA or MaNGA. \verb|piXedfit| can spatially match (in resolution and sampling) of the imaging and IFS data. By fitting only the photometric SEDs, \verb|piXedfit| can predict the spectral continuum, $\text{D}_{\rm n}4000$, $H_{\alpha}$, and $H_{\beta}$ well. The star formation rate (SFR) derived by \verb|piXedfit| is consistent with that derived from $H_{\alpha}$ emission. The RDSPS method gives equally good fitting results as the MCMC and it is much faster than the MCMC. \verb|piXedfit| is a versatile tool equipped with a parallel computing module for efficient analysis of large datasets, and will be made publicly available (this https URL).