Estimating Photometric Redshifts for X-ray sources in the X-ATLAS field, using machine-learning techniques
G. Mountrichas, A. Corral, V. A. Masoura, I. Georgantopoulos, A. Ruiz,, A. Georgakakis, F. J. Carrera, S. Fotopoulou

TL;DR
This paper develops a machine learning approach to estimate photometric redshifts for X-ray sources in the X-ATLAS field, improving accuracy by incorporating multi-wavelength photometry and source morphology.
Contribution
It introduces a novel application of TPZ machine learning to X-ray sources, demonstrating enhanced redshift estimation with multi-band data and morphological classification.
Findings
Achieved normalized median deviation of 0.06 in redshift estimates.
Reduced outlier percentage to 10-14% with multi-wavelength data.
Provided photometric redshifts for 933 X-ray sources in the field.
Abstract
We present photometric redshifts for 1,031 X-ray sources in the X-ATLAS field, using the machine learning technique TPZ (Carrasco Kind & Brunner 2013). X-ATLAS covers 7.1 deg2 observed with the XMM-Newton within the Science Demonstration Phase (SDP) of the H-ATLAS field, making it one of the largest contiguous areas of the sky with both XMMNewton and Herschel coverage. All of the sources have available SDSS photometry while 810 have additionally mid-IR and/or near-IR photometry. A spectroscopic sample of 5,157 sources primarily in the XMM/XXL field, but also from several X-ray surveys and the SDSS DR13 redshift catalogue, is used for the training of the algorithm. Our analysis reveals that the algorithm performs best when the sources are split, based on their optical morphology, into point-like and extended sources. Optical photometry alone is not enough for the estimation of accurate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
