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

Understand

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.