pixedfit 0.1

A python package specially designed for SED fitting of resolved sources

piXedfit

piXefit is a Python package that provides a complete 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 which can handle all tasks in the analysis of spatially resolved SEDs of a galaxy, 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 result. 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 performance is presented in Abdurro'uf et al. (2020, submitted).

Documentation of piXedfit can be found at this website. To get sense on how piXedfit works, the folder examples contains a demonstration on how to use piXedfit for deriving spatially resolved stellar population properties of a galaxy using 12-band imaging data from GALEX+SDSS+2MASS+WISE and the IFS data from CALIFA survey.

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 resolution and spatial sampling) of multiband images ranging from the 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 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 by 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 still be preserved, while increasing the S/N of the spatially resolved SEDs. The S/N threshold can be set to every band, not only 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 for any kind of input SED, either spatially resolved SED or integrated SED. The input can be in the form of photometric SED or spetrophotometric SED (i.e., combination of photometry and spectroscopy).

• 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., plot showing 1D and joint 2D posteriors of the parameters space), SED plot, and SFH plot.

How to get the code

Currently, this Python package is only available within the collaboration. We will make piXedfit publicly available in timely manner. In the meantime, if you are interested in using piXedfit, please contact Abdurro'uf at [e-mail]. We are very welcome to any ideas of new researches using piXedfit and we are open for collaboration.

Reference

A list of some projects piXedfit is benefitted from:

• FSPS and Python-FSPS stellar population synthesis model
• emcee package for the Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler
• Astropy
• Photutils
• Aniano et al. (2011) who provides convolution kernels for the PSF matching
• SExtractor (Bertin & Arnouts 1996)
• Abdurro'uf & Akiyama (2017, 2018)