X-Ray Redshifts of Obscured Chandra Source Catalog AGN
Dominic Sicilian, Francesca Civano, Nico Cappelluti, Johannes Buchner,, Alessandro Peca

TL;DR
This paper develops an X-ray based method to estimate redshifts of obscured AGN, using machine learning to improve accuracy and create a new redshift catalog for sources lacking documented measurements, aiding future X-ray missions.
Contribution
The study introduces a neural network classifier to enhance XZ redshift estimates and applies it to produce a new catalog of obscured AGN redshifts from Chandra data.
Findings
Neural network improves redshift estimation accuracy by ~3σ.
Created a catalog of 121 obscured AGN redshifts without prior measurements.
Nearly 90% of estimates are consistent with spectroscopic or photometric data.
Abstract
We have computed obscured AGN redshifts using the XZ method, adopting a broad treatment in which we employed a wide-ranging data set and worked primarily at the XZ counts sensitivity threshold, culminating with a redshift catalog containing 121 sources that lack documented redshifts. We considered 363 obscured AGN from the Chandra Source Catalog Release 2.0, 59 of which were selected using multiwavelength criteria while 304 were X-ray selected. One-third of the data set had cross-matched spectroscopic or photometric redshifts. These sources, dominated by low- and low- AGN, were supplemented by 1000 simulations to form a data set for testing the XZ method. We used a multi-layer perceptron neural network to examine and predict cases in which XZ fails to reproduce the known redshift, yielding a classifier that can identify and discard poor redshift estimates. This classifier…
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Taxonomy
TopicsAstrophysical Phenomena and Observations · Astronomical Observations and Instrumentation · Astronomy and Astrophysical Research
