Galaxy-multiplet clustering from DESI DR2

ABSTRACT We present an efficient estimator for higher order galaxy clustering using small groups of nearby galaxies, or multiplets. Using the Luminous Red Galaxy sample from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we identify galaxy multiplets as discrete objects and measure their cross-correlations with the general galaxy field. Our results show that the multiplets exhibit stronger clustering bias as they trace more massive dark matter haloes than individual galaxies. When comparing the observed clustering statistics with the mock catalogues generated from the N-body simulation AbacusSummit, we find that the mocks underpredict multiplet clustering despite reproducing the galaxy two-point autocorrelation reasonably well. This discrepancy indicates that the standard Halo Occupation Distribution (HOD) model is insufficient to describe the properties of galaxy multiplets, revealing the greater constraining power of this higher order statistic on galaxy–halo connection and the possibility that multiplets are specific to additional assembly bias. We demonstrate that incorporating secondary biases into the HOD model improves agreement with the observed multiplet statistics, specifically by allowing galaxies to preferentially occupy haloes in denser environments. Our results highlight the potential of utilizing multiplet clustering, beyond traditional two-point correlation measurements, to break degeneracies in models describing the galaxy–dark matter connection.

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Publication Details

Journal
Monthly Notices of the Royal Astronomical Society
Published
2025-12-02
DOI
https://doi.org/10.1093/mnras/staf2069
Primary Topic
Galaxies: Formation, Evolution, Phenomena
Type
article
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article

Galaxy-multiplet clustering from DESI DR2

Will J. Percival, R. R. Joyce, Rongpu Zhou, S. Juneau et al.
Monthly Notices of the Royal Astronomical Society
Galaxies: Formation, Evolution, Phenomena
article

Galaxy-multiplet clustering from DESI DR2

Will J. Percival, R. R. Joyce, Rongpu Zhou, S. Juneau, Michael S. Schubnell, Graziano Rossi, Eva-Maria Mueller, N. Palanque‐Delabrouille, S. Nadathur, Gregory Tarlé, Andreu Font-Ribera, Benjamin Alan Weaver, David Sprayberry, Gustavo Niz, D. Bianchi, David J. Schlegel, Ignasi Pérez-Ràfols, Axel de la Macorra, Martin Landriau, Simone Ferraro, Hanyue Wang, Gaston Gutierrez, J. Aguilar, Eusebio Sanchez, Ashley J. Ross, David Kirkby, ENRIQUE GAZTAÑAGA, Daniel J. Eisenstein, J. Silber, Arjun T. Dey, J. E. Forero-Romero, Steven P. Ahlen, Aaron Meisner, R. Miquel, Theodore S. Kisner, Biprateep Dey, David H. Brooks, Mustapha Ishak, C. Lamman, Hu Zou, T. Claybaugh, Marc Manera, P. Doel, O. Lahav, Francisco Prada, Klaus Honscheid
article en

Abstract

ABSTRACT We present an efficient estimator for higher order galaxy clustering using small groups of nearby galaxies, or multiplets. Using the Luminous Red Galaxy sample from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we identify galaxy multiplets as discrete objects and measure their cross-correlations with the general galaxy field. Our results show that the multiplets exhibit stronger clustering bias as they trace more massive dark matter haloes than individual galaxies. When comparing the observed clustering statistics with the mock catalogues generated from the N-body simulation AbacusSummit, we find that the mocks underpredict multiplet clustering despite reproducing the galaxy two-point autocorrelation reasonably well. This discrepancy indicates that the standard Halo Occupation Distribution (HOD) model is insufficient to describe the properties of galaxy multiplets, revealing the greater constraining power of this higher order statistic on galaxy–halo connection and the possibility that multiplets are specific to additional assembly bias. We demonstrate that incorporating secondary biases into the HOD model improves agreement with the observed multiplet statistics, specifically by allowing galaxies to preferentially occupy haloes in denser environments. Our results highlight the potential of utilizing multiplet clustering, beyond traditional two-point correlation measurements, to break degeneracies in models describing the galaxy–dark matter connection.

Monthly Notices of the Royal Astronomical SocietyVol. 545(4)
Boston University (US), Institució Catalana de Recerca i Estudis Avançats (ES), Universidad Complutense de Madrid (ES), Universitat Autònoma de Barcelona (ES), Universidad de Guanajuato (MX), Fermi National Accelerator Laboratory (US), Institute of Space Sciences (ES), Lawrence Berkeley National Laboratory (US), University of Waterloo (CA), State Street (United States) (US), Universidad de Los Andes (CO), The University of Texas at Dallas (US), University of Sussex (GB), University of Pittsburgh (US), Perimeter Institute (CA), University of Toronto (CA), Institute for High Energy Physics (ES), University of Milan (IT), Chinese Academy of Sciences (CN), University of California, Irvine (US), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), University of Michigan (US), Brera Astronomical Observatory (IT), Sejong University (KR), Institut d'Estudis Espacials de Catalunya (ES), Kavli Institute for Particle Astrophysics and Cosmology (US), Institute of Space Sciences (ES), Canadian Institute for Theoretical Astrophysics (CA), Center for Astrophysics Harvard & Smithsonian (US), CEA Paris-Saclay (FR), Community Science and Data Center (US), Centro de Investigación en Materiales Avanzados (MX), NSF NOIRLab (US), Universidad de Los Andes (BO), 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), University of California, Berkeley (US), Universitat Politècnica de Catalunya (ES), Stanford University (US)
Openalex Percentile: Top 74%
Galaxies: Formation, Evolution, Phenomena
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