Published November 26, 2019 | Version v1
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Modelling and interpreting the multi-wavelength spectral energy distributions of galaxies with machine learning and Bayesian inference

  • 1. Yunnan Observatories, Chinese Academy of Sciences; University of Science and Technology of China

Description

The galaxy spectral energy distributions (SEDs) from far-UV to far-IR are very important source of information about the properties of its stellar population, interstellar gas and dust, and AGN. To better understand the complex interplay among the three important physical components during the formation and evolution of galaxies, we need a reliable and efficient method and tool to extract useful information about them from the huge amount of data sets stemming from both ground- and space-based missions. To this end, with the combination of machine learning techniques and Bayesian inference, we have built the BayeSED code. In this talk, I will introduce the next generation of our BayeSED code which is capable of efficiently modeling and interpreting the full far-UV to far-IR SEDs of galaxies. 

Notes

BayeSED code, including the machine learning module, is publicly available at https://bitbucket.org/hanyk/bayesed/. See also the documents at http://bayesed.readthedocs.io/.

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Is derived from
Journal article: 2019ApJS..240....3H (Bibcode)
Journal article: 2014ApJS..215....2H (Bibcode)
Journal article: 2012ApJ...749..123H (Bibcode)