Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

Abstract We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of principal component analysis–derived templates, which is very efficient in processing large volumes of DESI spectra within a short time frame. However, this method occasionally yields unphysical model fits for galaxies and fails to adequately absorb calibration errors that may still be occasionally visible in the reduced spectra. Our proposed approach improves upon this existing method by refitting the spectra with carefully generated physical galaxy archetypes combined with additional terms designed to absorb data reduction defects and provide more physical models to the DESI spectra. We test our method on an extensive data set derived from the survey validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the new method delivers marginally better redshift success for SV tiles while reducing catastrophic redshift failure by 10%–30%. At the same time, results from millions of targets from the main survey show that our model has relatively higher redshift success and purity rates (0.5%–0.8% higher) for galaxy targets while having similar success for QSOs. These improvements also demonstrate that the main DESI redshift pipeline is generally robust. Additionally, it reduces the false-positive redshift estimation by 5%−40% for sky fibers. We also discuss the generic nature of our method and how it can be extended to other large spectroscopic surveys, along with possible future improvements.

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Publication Details

Journal
The Astronomical Journal
Published
2024-08-21
DOI
https://doi.org/10.3847/1538-3881/ad60c2
Citations
18
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
Field-Weighted Citation Impact
5.97

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article

Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

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18 citations
The Astronomical Journal
Galaxies: Formation, Evolution, Phenomena
5.97
article

Archetype-based Redshift Estimation for the Dark Energy Spectroscopic Instrument Survey

D. Kirkby, Will J. Percival, Andrew Lambert, A. Bolton, Abhijeet Anand, D. Brooks, Rongpu Zhou, S. Juneau, Anand Raichoor, Michael S. Schubnell, Graziano Rossi, Eva-Maria Mueller, N. Palanque‐Delabrouille, Gregory Tarlé, C. Warner, Laurent Le Guillou, David Sprayberry, Gustavo Niz, David J. Schlegel, Axel de la Macorra, S. Bailey, Martin Landriau, A. Brodzeller, Shaun Cole, Marc Manera, Gaston Gutierrez, J. Aguilar, Anthony Kremin, J. Guy, Eusebio Sanchez, Satya Gontcho A Gontcho, ENRIQUE GAZTAÑAGA, Edward F. Schlafly, K. Honscheid, Cullan Howlett, K. Fanning, J. E. Forero-Romero, Steven P. Ahlen, Aaron Meisner, R. Miquel, Theodore S. Kisner, Biprateep Dey, Mehdi Rezaie, John Moustakas, Claire Poppett, Hu Zou, T. Claybaugh, B. A. Weaver, Francisco Prada
article en
18 citations

Abstract

Abstract We present a computationally efficient galaxy archetype-based redshift estimation and spectral classification method for the Dark Energy Survey Instrument (DESI) survey. The DESI survey currently relies on a redshift fitter and spectral classifier using a linear combination of principal component analysis–derived templates, which is very efficient in processing large volumes of DESI spectra within a short time frame. However, this method occasionally yields unphysical model fits for galaxies and fails to adequately absorb calibration errors that may still be occasionally visible in the reduced spectra. Our proposed approach improves upon this existing method by refitting the spectra with carefully generated physical galaxy archetypes combined with additional terms designed to absorb data reduction defects and provide more physical models to the DESI spectra. We test our method on an extensive data set derived from the survey validation (SV) and Year 1 (Y1) data of DESI. Our findings indicate that the new method delivers marginally better redshift success for SV tiles while reducing catastrophic redshift failure by 10%–30%. At the same time, results from millions of targets from the main survey show that our model has relatively higher redshift success and purity rates (0.5%–0.8% higher) for galaxy targets while having similar success for QSOs. These improvements also demonstrate that the main DESI redshift pipeline is generally robust. Additionally, it reduces the false-positive redshift estimation by 5%−40% for sky fibers. We also discuss the generic nature of our method and how it can be extended to other large spectroscopic surveys, along with possible future improvements.

The Astronomical JournalVol. 168(3)
Boston University (US), Institució Catalana de Recerca i Estudis Avançats (ES), Universidad Complutense de Madrid (ES), Universitat Autònoma de Barcelona (ES), Centre National de la Recherche Scientifique (FR), Space Telescope Science Institute (US), Universidad de Guanajuato (MX), Fermi National Accelerator Laboratory (US), Institute of Space Sciences (ES), Lawrence Berkeley National Laboratory (US), University of Waterloo (CA), Universidad de Los Andes (CO), University of Sussex (GB), The University of Queensland (AU), University of Pittsburgh (US), Perimeter Institute (CA), Institute for High Energy Physics (ES), Kansas State University (US), Durham University (GB), Chinese Academy of Sciences (CN), University of California, Irvine (US), University of Utah (US), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), University of Michigan (US), SLAC National Accelerator Laboratory (US), Sejong University (KR), University of Florida (US), Sorbonne Université (FR), Institut d'Estudis Espacials de Catalunya (ES), Kavli Institute for Particle Astrophysics and Cosmology (US), Laboratoire de Physique Nucléaire et de Hautes Énergies (FR), Université Bourgogne Franche-Comté (FR), CEA Paris-Saclay (FR), Institut National de Physique Nucléaire et de Physique des Particules (FR), Community Science and Data Center (US), NSF NOIRLab (US), Instituto de Astrofísica de Andalucía (ES), National Astronomical Observatories (CN), Institut de Recherche sur les Lois Fondamentales de l'Univers (FR), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), University College London (GB), The Ohio State University (US), University of Portsmouth (GB), Universidad Nacional Autónoma de México (MX), Siena College (US), University of California, Berkeley (US)
National Science Foundation, U.S. Department of Energy, Gordon and Betty Moore Foundation, National Energy Research Scientific Computing Center, Ministerio de Ciencia e Innovación, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Office of Science, Science and Technology Facilities Council, Division of Astronomical Sciences, Lawrence Berkeley National Laboratory
Affordable and clean energy
Openalex Percentile: Top 4%
Galaxies: Formation, Evolution, Phenomena
5.97
DOIOpen Access PDFOpenAlex
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