pixedfit 0.1.1

A python package specially designed for SED fitting of resolved sources

piXedfit

piXefit is a Python package that provides a self-contained set of tools for analyzing spatially resolved properties of galaxies using imaging data or a combination of imaging data and the integral field spectroscopy (IFS) data. piXedfit has six modules that can handle all tasks in the analysis of the spatially resolved SEDs of galaxies, including images processing, a spatial-matching between reduced broad-band images with an IFS data cube, pixel binning, performing SED fitting, and making visualization plots for the SED fitting results. piXedfit is a versatile tool that has been equipped with the multiprocessing module, namely message passing interface or MPI, for efficient analysis of the datasets of a large number of galaxies. Detailed description on piXedfit and demonstration of its performances are presented in Abdurro'uf et al. (2021).

Detailed documentation of piXedfit is given at this website. To get the first glance of how piXedfit works, the folder examples contains demonstrations on how to use this tool for measuring spatially resolved stellar population properties of a galaxy using a combination of 12-band imaging data (from GALEX, SDSS, 2MASS, and WISE) and IFS data from CALIFA.

Some demo can be seen from: pixel binning and SED fitting.

Features

piXedfit has 6 modules that can work independent with each other such that a user interested of using a particular module in piXedfit doesn't need to use the other modules. For instance, it is possible to use the SED fitting module for fitting any observed SED, either integrated of spaially resolved SED, without the need of using the image processing and pixel binning modules. The 6 modules and their usabilities are the following:

• piXedfit_images: image processing

  This module is capable of doing spatial-matching (in spatial resolution and spatial sampling) of multiband imaging data ranging from FUV to FIR (from ground-based and spaced-based telescopes) and extract pixel-wise photometric SEDs within the galaxy's region of interest.

• piXedfit_spectrophotometric: spatial-matching of imaging data and the IFS data

  This module is capable of doing spatial-matching (in spatial resolution and sampling) of a multiband imaging data (that have been processed by the piXedfit_images) with an IFS data cube (containing the same galaxy) and extract pixel-wise spectrophotometric SEDs within the galaxy's region of interest. For the current version of piXedfit, only the IFS data from the CALIFA and MaNGA surveys can can be analyzed with the piXedfit_spectrophotometric module.

• piXedfit_bin: pixel binning

  This module is capable of performing pixel binning, which is a process of combining neighboring pixels to achieve certain S/N thresholds. The pixel binning scheme takes into account the similarity of SED shape among the pixels that are going to be binned together. This way important spatial information from the pixel scale can be expected to be preserved. The S/N threshold can be set to all bands, not limited to a particular band.

• piXedfit_model: generating model SEDs

  This module can generate model SEDs of galaxies given some parameters. The SED modeling uses the FSPS SPS model with the Python-FSPS as the interface to the Python environment. The SED modeling incorporates the modeling of light coming from stellar emission, nebular emission, dust emission, and the AGN dusty torus emission.

• piXedfit_fitting: performing SED fitting

  This module is capable of performing SED fitting to input SEDs of either spatially resolved (i.e., kpc-scale) or integrated (galaxy's global scale) SEDs. This SED fitting module can perform a simultaneous fitting of photometric and spectroscopic SEDs (i.e., spectrophotometric SED).

• piXedfit_analysis: making visualization plots for the SED fiting results

  This module can make three plots for visualizing the fitting results: corner plot (i.e., a plot showing 1D and joint 2D posteriors of the parameters), SED plot (i.e., a plot showing recovery of the input SED by the best-fit model SED), and SFH plot (i.e., a plot showing inferred SFH from the fitting).

Kernels

Because of the large sizes of the kernel files, we do not upload them to this GitHub repository. We put the kernel files on this link. To be able to use image processing feature (piXedfit_images), you need to download the necessary kernel files and copy them to /data/kernels. List of the kernel files needed for image processing would depend on the imaging data that will be analyzed.

How to get the code

Currently, we are working on documentation of piXedfit and we plan to publicly release the codes in the next few months (before summer 2022). In the meantime, if you are interested in using piXedfit, please contact Abdurro'uf ([e-mail]).

Citation

If you use this code for your research, please reference Abdurro'uf et al. (2021):

@ARTICLE{2021ApJS..254...15A,
       author = {{Abdurro'uf} and {Lin}, Yen-Ting and {Wu}, Po-Feng and {Akiyama}, Masayuki},
        title = "{Introducing piXedfit: A Spectral Energy Distribution Fitting Code Designed for Resolved Sources}",
      journal = {\apjs},
     keywords = {Astronomical methods, Bayesian statistics, Galaxy evolution, Posterior distribution, 1043, 1900, 594, 1926, Astrophysics - Astrophysics of Galaxies},
         year = 2021,
        month = may,
       volume = {254},
       number = {1},
          eid = {15},
        pages = {15},
          doi = {10.3847/1538-4365/abebe2},
archivePrefix = {arXiv},
       eprint = {2101.09717},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021ApJS..254...15A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Reference

A list of some projects piXedfit is benefitted from:

• Astropy
• Photutils
• Aniano et al. (2011) who provides convolution kernels
• FSPS and Python-FSPS stellar population synthesis model
• emcee package for the Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler