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SRGz: Building an Optical Cross-Match Model for the X-ray SRG/eROSITA Sources Using the Lockman Hole Data

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Abstract

We present a probabilistic model built for the optical cross-match between the SRG/eROSITA X-ray sources and photometric data from the DESI Legacy Imaging Surveys. The model relies both on positional and photometric information on optical objects nearby X-ray sources and allows performing selection with precision and recall \({\approx}94\%\) (for \(F_{\textrm{X,0.5-2}}>10^{-14}\) erg s\({}^{-1}\) cm\({}^{-2}\)). With this model, we calibrated positional error of the SRG/eROSITA sources detected in the Lockman Hole: \(\sigma_{\textrm{corr}}=0.87\sqrt{\sigma_{\textrm{det}}^{2.53}+1.12^{2}}\). The model will become a part of the SRGz system for data analysis of the X-ray data obtained from the all-sky SRG/eROSITA survey.

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Notes

  1. \(\mathcal{L}=-\ln(p)\), where \(p\) is a null hypothesis probability for a source to be produced by the noise component on an X-ray image.

  2. The search radius \(r_{\textrm{false}}\) was calculated as \(r_{\textrm{false}}=(-\ln[1-f_{\textrm{thresh}}]\pi^{-1}\rho_{\textrm{desi}}^{-1})^{1/2}=1.43^{\prime\prime}\), where \(\rho_{\text{desi}}\approx 4.7\times 10^{-3}\text{arcsec}^{-2}\) is an average sky density of the DESI LIS objects within LH, \(f_{\textrm{thresh}}=0.03\) is the probability to find one source or more by chance within \(r<r_{\textrm{false}}\) according to the Poisson distribution with \(\lambda=\rho_{\textrm{desi}}\).

  3. We used SciServer API for Python https://github.com/sciserver/SciScript-Python.

  4. We chose the following \(p_{\textrm{lim}}\) values: \(0.5\), \(0.55\), \(0.6\), \(0.65\), \(0.7\), \(0.75\), \(0.8\), \(0.85\), \(0.9\), \(0.95\), \(0.99\), \(0.995\).

  5. http://cda.cfa.harvard.edu/cscview/

  6. http://www.sciserver.org

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Belvedersky, M.I., Meshcheryakov, A.V., Medvedev, P.S. et al. SRGz: Building an Optical Cross-Match Model for the X-ray SRG/eROSITA Sources Using the Lockman Hole Data. Astron. Lett. 48, 109–125 (2022). https://doi.org/10.1134/S1063773722020013

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