Lightning: An X-ray to Submillimeter Galaxy SED Fitting Code With Physically-Motivated Stellar, Dust, and AGN Models

Date

2023-04-13

Department

Program

Citation of Original Publication

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

Abstract

We present an updated version of Lightning, a galaxy spectral energy distribution (SED) fitting code that can model X-ray to submillimeter observations. The models in Lightning include the options to contain contributions from stellar populations, dust attenuation and emission, and active galactic nuclei (AGN). X-ray emission, when utilized, can be modeled as originating from stellar compact binary populations with the option to include emission from AGN. We have also included a variety of algorithms to fit the models to observations and sample parameter posteriors; these include an adaptive Markov-Chain Monte-Carlo (MCMC), affine-invariant MCMC, and Levenberg-Marquardt gradient decent (MPFIT) algorithms. To demonstrate some of the capabilities of Lightning, we present several examples using a variety of observational data. These examples include (1) deriving the spatially resolved stellar properties of the nearby galaxy M81, (2) demonstrating how X-ray emission can provide constrains on the properties of the supermassive black hole of a distant AGN, (3) exploring how to rectify the attenuation effects of inclination on the derived the star formation rate of the edge-on galaxy NGC 4631, (4) comparing the performance of Lightning to similar Bayesian SED fitting codes when deriving physical properties of the star-forming galaxy NGC 628, and (5) comparing the derived X-ray and UV-to-IR AGN properties from Lightning and CIGALE for a distant AGN. Lightning is an open-source application developed in the Interactive Data Language (IDL) and is available at https://github.com/rafaeleufrasio/lightning.