Probabilistic photo-z machine learning models for X-ray sky surveys

Accurate photo-z measurements are important to construct a large-scale structure map of X-ray Universe in the ongoing SRG/eROSITA All-Sky Survey. We present machine learning Random Forest-based models for probabilistic photo-z predictions based on information from 4 large photometric surveys (SDSS,...

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Bibliographic Details
Published in:arXiv.org 2021-07
Main Authors: Borisov, Viktor, Meshcheryakov, Alex, Gerasimov, Sergey, RU eROSITA catalog group
Format: Article
Language:English
Subjects:
Galaxies
Interstellar extinction
Large scale structure of the universe
Machine learning
Outliers (statistics)
Photometry
Quasars
Sky surveys (astronomy)
X ray sources
X-ray astronomy
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Summary:Accurate photo-z measurements are important to construct a large-scale structure map of X-ray Universe in the ongoing SRG/eROSITA All-Sky Survey. We present machine learning Random Forest-based models for probabilistic photo-z predictions based on information from 4 large photometric surveys (SDSS, Pan-STARRS, DESI Legacy Imaging Survey, and WISE). Our models are trained on the large sample of \(\approx\)580000 quasars and galaxies selected from the SDSS DR14 spectral catalog and take into account Galactic extinction and uncertainties in photometric measurements for target objects. On the Stripe82X test sample we obtained photo-z accuracy for X-ray sources: \(NMAD=0.034\) (normalized median absolute deviation) and \(n_{>0.15}=0.088\) (catastrophic outliers fraction), which is almost \(\sim2\) times better than best photo-z results available in the literature.
ISSN:2331-8422