CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

¹ Max-Planck-Institut für extraterrestrische Physik, Giessenbachstr. 1, 85748 Garching, Germany
² Technical University of Munich Department of Computer Science – I26, Boltzmannstr. 3, 85748 Garching b. München, Germany
³ Exzellenzcluster ORIGINS, Boltzmannstr. 2, D-85748 Garching, Germany
⁴ LMU Munich, Arnold Sommerfeld Center for Theoretical Physics, Theresienstr. 37, 80333 München, Germany
⁵ LMU Munich, Universität-Sternwarte, Scheinerstr. 1, 81679 München, Germany
⁶ Cerro Tololo Inter-American Observatory/NSF’s NOIRLab, Casilla 603, La Serena, Chile
⁷ Perimeter Institute for Theoretical Physics, 31 Caroline St. North, Waterloo, ON N2L 2Y5, Canada
⁸ Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
⁹ National Optical Astronomy Observatory, 950 N. Cherry Avenue, Tucson, AZ 85719, USA
¹⁰ NSF National Optical/Infrared Research Laboratory, 950 N. Cherry Ave, Tucson, AZ 85719, USA
¹¹ Department of Physics & Astronomy, University of Wyoming, 1000 E. University, Department 3905, Laramie, WY 8207, USA
¹² Instituto de Estudios Astrofísicos, Facultad de Ingeniería y Ciencias, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago, Chile
¹³ Instituto de Estudios Astrofísicos, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago 8370191, Chile
¹⁴ Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, China
¹⁵ Núcleo de Astronomía de la Facultad de Ingeniería, Universidad Diego Portales, Av. Ejército Libertador 441, Santiago 22, Chile
¹⁶ Space Telescope Science Institute, 3700 San Martin Drive, Baltimore MD 21218, USA
¹⁷ Department of Astronomy, University of Washington, Box 351580, Seattle, WA 98195, USA
¹⁸ Department of Astronomy, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
¹⁹ National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
²⁰ Center for Artificial Intelligence Innovation, University of Illinois at Urbana-Champaign, 1205 West Clark Street, Urbana, IL 61801, USA

^⋆ Corresponding author; mara@mpe.mpg.de

Received: 27 May 2024
Accepted: 16 July 2024

Abstract

Context. Photometric redshifts for galaxies hosting an accreting supermassive black hole in their center, known as active galactic nuclei (AGNs), are notoriously challenging. At present, they are most optimally computed via spectral energy distribution (SED) fittings, assuming that deep photometry for many wavelengths is available. However, for AGNs detected from all-sky surveys, the photometry is limited and provided by a range of instruments and studies. This makes the task of homogenizing the data challenging, presenting a dramatic drawback for the millions of AGNs that wide surveys such as SRG/eROSITA are poised to detect.

Aims. This work aims to compute reliable photometric redshifts for X-ray-detected AGNs using only one dataset that covers a large area: the tenth data release of the Imaging Legacy Survey (LS10) for DESI. LS10 provides deep grizW1-W4 forced photometry within various apertures over the footprint of the eROSITA-DE survey, which avoids issues related to the cross-calibration of surveys.

Methods. We present the results from CIRCLEZ, a machine-learning algorithm based on a fully connected neural network. CIRCLEZ is built on a training sample of 14 000 X-ray-detected AGNs and utilizes multi-aperture photometry, mapping the light distribution of the sources.

Results. The accuracy (σ_NMAD) and the fraction of outliers (η) reached in a test sample of 2913 AGNs are equal to 0.067 and 11.6%, respectively. The results are comparable to (or even better than) what was previously obtained for the same field, but with much less effort in this instance. We further tested the stability of the results by computing the photometric redshifts for the sources detected in CSC2 and Chandra-COSMOS Legacy, reaching a comparable accuracy as in eFEDS when limiting the magnitude of the counterparts to the depth of LS10.

Conclusions. The method can be applied to fainter samples of AGNs using deeper optical data from future surveys (for example, LSST, Euclid), granting LS10-like information on the light distribution beyond the morphological type. Along with this paper, we have released an updated version of the photometric redshifts (including errors and probability distribution functions) for eROSITA/eFEDS.