PICZL: Image-based Photometric Redshifts for AGN

• URL: http://arxiv.org/abs/2411.07305v2
• Date: Wed, 13 Nov 2024 15:29:10 GMT
• Title: PICZL: Image-based Photometric Redshifts for AGN
• Authors: William Roster, Mara Salvato, Sven Krippendorf, Aman Saxena, Raphael Shirley, Johannes Buchner, Julien Wolf, Tom Dwelly, Franz E. Bauer, James Aird, Claudio Ricci, Roberto J. Assef, Scott F. Anderson, Xin Liu, Andrea Merloni, Jochen Weller, Kirpal Nandra,
• Abstract summary: We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. PICZL integrates distinct SED features from images with those obtained from catalog-level data. On a validation sample of 8098 AGN, PICZL achieves a variance $sigma_textrmNMAD$ of 4.5% with an outlier fraction $eta$ of 5.6%.
• Score: 1.5194351731792657
• License:
• Abstract: Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $\sigma_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $\eta$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

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