PICZL: Image-based photometric redshifts for AGN

Context . Computing reliable photometric redshifts (photo-z) for active galactic nuclei (AGN) is a challenging task, primarily due to the complex interplay between the unresolved relative emissions associated with the supermassive black hole and its host galaxy. Spectral energy distribution (SED) fitting methods, while effective for galaxies and AGN in pencil-beam surveys, face limitations in wide or all-sky surveys with fewer bands available, lacking the ability to accurately capture the AGN contribution to the SED, hindering reliable redshift estimation. This limitation is affecting the many tens of millions of AGN detected in existing datasets, such as those AGN clearly singled out and identified by SRG/eROSITA. Aims . Our goal is to enhance photometric redshift performance for AGN in all-sky surveys while simultaneously simplifying the approach by 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 the Dark Energy Spectroscopic Instrument, which covers >20 000 deg 2 of extragalactic sky with deep imaging and catalog-based photometry in the ɡriɀW1-W4 bands. We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. Methods . We introduce PICZL, a machine-learning algorithm leveraging an ensemble of convolutional neural networks. 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. Results . On a validation sample of 8098 AGN, PICZL achieves an accuracy σ NMAD of 4.5% with an outlier fraction η of 5.6%. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning. We highlight that the model’s performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Conclusions . 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 realization. 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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Publication Details

Journal
Astronomy and Astrophysics
Published
2024-11-13
DOI
https://doi.org/10.1051/0004-6361/202452361
Citations
2
Primary Topic
Satellite Image Processing and Photogrammetry
Type
article
Field-Weighted Citation Impact
0.67

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article

PICZL: Image-based photometric redshifts for AGN

James Aird, Scott F. Anderson, Cláudio Ricci, R. Shirley et al.
2 citations
Astronomy and Astrophysics
Satellite Image Processing and Photogrammetry
0.67
article

PICZL: Image-based photometric redshifts for AGN

James Aird, Scott F. Anderson, Cláudio Ricci, R. Shirley, T. Dwelly, F. E. Bauer, Roberto J. Assef, X. Liu, J. Wolf, M. Salvato, Sven Krippendorf, J. Weller, Aayush Saxena, K. Nandra, Johannes Büchner, A. Merloni, W. Roster
article en
2 citations

Abstract

Context . Computing reliable photometric redshifts (photo-z) for active galactic nuclei (AGN) is a challenging task, primarily due to the complex interplay between the unresolved relative emissions associated with the supermassive black hole and its host galaxy. Spectral energy distribution (SED) fitting methods, while effective for galaxies and AGN in pencil-beam surveys, face limitations in wide or all-sky surveys with fewer bands available, lacking the ability to accurately capture the AGN contribution to the SED, hindering reliable redshift estimation. This limitation is affecting the many tens of millions of AGN detected in existing datasets, such as those AGN clearly singled out and identified by SRG/eROSITA. Aims . Our goal is to enhance photometric redshift performance for AGN in all-sky surveys while simultaneously simplifying the approach by 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 the Dark Energy Spectroscopic Instrument, which covers >20 000 deg 2 of extragalactic sky with deep imaging and catalog-based photometry in the ɡriɀW1-W4 bands. We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. Methods . We introduce PICZL, a machine-learning algorithm leveraging an ensemble of convolutional neural networks. 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. Results . On a validation sample of 8098 AGN, PICZL achieves an accuracy σ NMAD of 4.5% with an outlier fraction η of 5.6%. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning. We highlight that the model’s performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Conclusions . 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 realization. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

Astronomy and AstrophysicsVol. 692
National Science Foundation, Smithsonian Institution, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Yale University, Carnegie Institution of Washington, University of Pennsylvania, Ohio State University, California Institute of Technology, Harvard University, University of Chicago, University of Michigan, University of Utah, Johns Hopkins University, University of Portsmouth, New Mexico State University, Space Telescope Science Institute, UK Research and Innovation, Heising-Simons Foundation, Carnegie Institution for Science, National Energy Research Scientific Computing Center, Flatiron Health, University College London, University of Nottingham, University of Sussex, University of Edinburgh, Deutsche Forschungsgemeinschaft, École Polytechnique Fédérale de Lausanne, Eberhard Karls Universität Tübingen, Chinese Academy of Sciences, University of Toronto, Max-Planck-Gesellschaft, Financiadora de Estudos e Projetos, Universität Hamburg, Universidad Nacional Autónoma de México, Yunnan University, Nanjing University, Rheinische Friedrich-Wilhelms-Universität Bonn, China National Textile and Apparel Council, Leibniz-Institut für Astrophysik Potsdam, Agencia Nacional de Investigación y Desarrollo, Max-Planck-Institut für Astronomie, University of Illinois at Urbana-Champaign, Office of Science, Jet Propulsion Laboratory, University of Colorado Boulder, Smithsonian Astrophysical Observatory, Science and Technology Facilities Council, Leibniz-Gemeinschaft, Russian Academy of Sciences, National Astronomical Observatories, Chinese Academy of Sciences, Division of Astronomical Sciences, High Energy Physics, Integrated Electronics Engineering Center, Binghamton University, Argonne National Laboratory, Fermilab, Lawrence Berkeley National Laboratory, SLAC National Accelerator Laboratory
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