Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment^⋆

¹ Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Madrid, Spain
e-mail: laura.toribio@ciemat.es
² Department of Astronomy, University of Geneva, ch. d’Écogia 16, 1290 Versoix, Switzerland
³ Department of Astrophysical Sciences, Princeton University, Peyton Hall, Princeton, NJ 08544, USA
⁴ Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁵ Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK
⁶ Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
⁷ NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
⁸ Center for Astrophysical Surveys, National Center for Supercomputing Applications, 1205 West Clark St., Urbana, IL 61801, USA
⁹ Department of Astronomy, University of Illinois at Urbana-Champaign, 1002 W. Green Street, Urbana, IL 61801, USA
¹⁰ School of Physics and Astronomy, Cardiff University, Cardiff CF24 3AA, UK
¹¹ Brookhaven National Laboratory, Bldg 510, Upton, NY 11973, USA
¹² Fermi National Accelerator Laboratory, PO Box 500 Batavia, IL 60510, USA
¹³ Laboratório Interinstitucional de e-Astronomia – LIneA, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
¹⁴ Department of Physics, University of Michigan, Ann Arbor, MI 48109, USA
¹⁵ Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX, UK
¹⁶ Department of Physics & Astronomy, University College London, Gower Street, London WC1E 6BT, UK
¹⁷ Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15312, USA
¹⁸ Instituto de Astrofisica de Canarias, 38205 La Laguna, Tenerife, Spain
¹⁹ Universidad de La Laguna, Dpto. Astrofísica, 38206 La Laguna, Tenerife, Spain
²⁰ Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Campus UAB, 08193 Bellaterra, Barcelona, Spain
²¹ Institut d’Estudis Espacials de Catalunya (IEEC), 08034 Barcelona, Spain
²² Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain
²³ Jodrell Bank Center for Astrophysics, School of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M13 9PL, UK
²⁴ University of Nottingham, School of Physics and Astronomy, Nottingham NG7 2RD, UK
²⁵ Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg, Germany
²⁶ School of Mathematics and Physics, University of Queensland, Brisbane, QLD 4072, Australia
²⁷ Department of Physics, IIT Hyderabad, Kandi, Telangana 502285, India
²⁸ Institute of Theoretical Astrophysics, University of Oslo, PO Box 1029 Blindern, 0315 Oslo, Norway
²⁹ Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL 60637, USA
³⁰ Instituto de Fisica Teorica UAM/CSIC, Universidad Autonoma de Madrid, 28049 Madrid, Spain
³¹ Santa Cruz Institute for Particle Physics, Santa Cruz, CA 95064, USA
³² Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA
³³ Department of Physics, The Ohio State University, Columbus, OH 43210, USA
³⁴ Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA
³⁵ Australian Astronomical Optics, Macquarie University, North Ryde, NSW 2113, Australia
³⁶ Lowell Observatory, 1400 Mars Hill Rd, Flagstaff, AZ 86001, USA
³⁷ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA
³⁸ Centre for Gravitational Astrophysics, College of Science, The Australian National University, ACT 2601, Australia
³⁹ The Research School of Astronomy and Astrophysics, Australian National University, ACT 2601, Australia
⁴⁰ George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, and Department of Physics and Astronomy, Texas A&M University, College Station, TX 77843, USA
⁴¹ LPSC Grenoble, 53 Avenue des Martyrs, 38026 Grenoble, France
⁴² Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain
⁴³ Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ 20921-400, Brazil
⁴⁴ Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, PO Box 2450 Stanford, CA 94305, USA
⁴⁵ SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
⁴⁶ School of Physics and Astronomy, University of Southampton, Southampton SO17 1BJ, UK
⁴⁷ Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
⁴⁸ Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA

Received: 14 December 2023
Accepted: 24 February 2024

Abstract

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches is machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs.

Aims. The goal of this work is to calculate the photo-zs for the Year 3 (Y3) Dark Energy Survey (DES) Deep Fields catalogue using the Directional Neighborhood Fitting (DNF) machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained by DNF on the Y3 DES Deep Fields catalogue with that of the EAzY template fitting approach.

Methods. We emulated – at a brighter magnitude – the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We used a principal component analysis to graphically assess the incompleteness and relate it with the performance parameters provided by DNF. Finally, we applied the results on the incompleteness to the photo-z computation on the Y3 DES Deep Fields with DNF and estimated its performance.

Results. The photo-zs of the galaxies in the DES deep fields were computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. We have developed some techniques to evaluate the performance in the absence of “true” redshift and to assess the completeness. We have studied the tradeoff in the training sample between the highest spectroscopic redshift quality versus completeness. We found some advantages in relaxing the highest-quality spectroscopic redshift requirements at fainter magnitudes in favour of completeness. The results achieved by DNF on the Y3 Deep Fields are competitive with the ones provided by EAzY, showing notable stability at high redshifts. It should be noted that the good results obtained by DNF in the estimation of photo-zs in deep field catalogues make DNF suitable for the future Legacy Survey of Space and Time (LSST) and Euclid data, which will have similar depths to the Y3 DES Deep Fields.