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Title: DESI mock challenge: Halo and galaxy catalogues with the bias assignment method

Abstract

We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). The construction of the catalogues shown in this paper is part of a mock-comparison project within the Dark Energy Spectroscopic Instrument (DESI) collaboration. We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed N-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. We introduce cosmic-web-dependent corrections to modelling redshift-space distortions at the N-body level – both in the halo and galaxy distributions –, as well as a multi-scale approach for accurate assignment of halo properties. Using specific models of halo occupation distributions to populate halos, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. BAM generates mock catalogues with per centmore » accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display ~3%-10% accuracy in the multipoles of the power spectrum up to scales of k ~ 0.4 h-1Mpc. We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys.« less

Authors:
ORCiD logo [1];  [1];  [2];  [3];  [4];  [1];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [8];  [16]
+ Show Author Affiliations
  1. Instituto de Astrofísica de Canarias, La Laguna (Spain); Universidad de La Laguna (Spain)
  2. University of Edinburgh, Scotland (United Kingdom). The Royal Observatory; Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai (India)
  3. Stanford University, CA (United States)
  4. Shanghai Jiao Tong University (China)
  5. Instituto de Astrofísica de Canarias, La Laguna (Spain); Universidad de La Laguna (Spain); Università degli Studi di Padova (Italy)
  6. Ecole Polytechnique Federale Lausanne (EPFL) (Switzerland)
  7. University College London (United Kingdom)
  8. Universidad Nacional Autónoma de México, Ciudad de México (México)
  9. Universitat Autonoma de Barcelona (Spain)
  10. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
  11. The Ohio State University, Columbus, OH (United States)
  12. Southern Methodist University, Dallas, TX (United States)
  13. NSF’s NOIRLab, Tucson, AZ (United States)
  14. Universitat Autonoma de Barcelona (Spain); Institució Catalana de Recerca i Estudis Avançats, Barcelona (Spain)
  15. University of Michigan, Ann Arbor, MI (United States)
  16. Chinese Academy of Sciences (CAS), Beijing (China)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); National Science Foundation (NSF)
OSTI Identifier:
2212459
Grant/Contract Number:  
AC02-05CH11231; AST-0950945
Resource Type:
Accepted Manuscript
Journal Name:
Astronomy and Astrophysics
Additional Journal Information:
Journal Volume: 673; Journal ID: ISSN 0004-6361
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; methods: numerical; Galaxy: halo; galaxies: statistics

Citation Formats

Balaguera-Antolínez, Andrés, Kitaura, Francisco-Shu, Alam, Shadab, Chuang, Chia-Hsun, Yu, Yu, Favole, Ginevra, Sinigaglia, Francesco, Zhao, Cheng, Brooks, David, de la Macorra, Axel, Font-Ribera, Andreu, Gontcho A Gontcho, Satya, Honscheid, Klaus, Kehoe, Robert, Meisner, Aron, Miquel, Ramon, Tarlé, Gregory, Vargas-Magaña, Mariana, and Zhou, Zhimin. DESI mock challenge: Halo and galaxy catalogues with the bias assignment method. United States: N. p., 2023. Web. doi:10.1051/0004-6361/202245618.
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Balaguera-Antolínez, Andrés, Kitaura, Francisco-Shu, Alam, Shadab, Chuang, Chia-Hsun, Yu, Yu, Favole, Ginevra, Sinigaglia, Francesco, Zhao, Cheng, Brooks, David, de la Macorra, Axel, Font-Ribera, Andreu, Gontcho A Gontcho, Satya, Honscheid, Klaus, Kehoe, Robert, Meisner, Aron, Miquel, Ramon, Tarlé, Gregory, Vargas-Magaña, Mariana, & Zhou, Zhimin. DESI mock challenge: Halo and galaxy catalogues with the bias assignment method. United States. https://doi.org/10.1051/0004-6361/202245618
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Balaguera-Antolínez, Andrés, Kitaura, Francisco-Shu, Alam, Shadab, Chuang, Chia-Hsun, Yu, Yu, Favole, Ginevra, Sinigaglia, Francesco, Zhao, Cheng, Brooks, David, de la Macorra, Axel, Font-Ribera, Andreu, Gontcho A Gontcho, Satya, Honscheid, Klaus, Kehoe, Robert, Meisner, Aron, Miquel, Ramon, Tarlé, Gregory, Vargas-Magaña, Mariana, and Zhou, Zhimin. Thu . "DESI mock challenge: Halo and galaxy catalogues with the bias assignment method". United States. https://doi.org/10.1051/0004-6361/202245618. https://www.osti.gov/servlets/purl/2212459.
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@article{osti_2212459,
title = {DESI mock challenge: Halo and galaxy catalogues with the bias assignment method},
author = {Balaguera-Antolínez, Andrés and Kitaura, Francisco-Shu and Alam, Shadab and Chuang, Chia-Hsun and Yu, Yu and Favole, Ginevra and Sinigaglia, Francesco and Zhao, Cheng and Brooks, David and de la Macorra, Axel and Font-Ribera, Andreu and Gontcho A Gontcho, Satya and Honscheid, Klaus and Kehoe, Robert and Meisner, Aron and Miquel, Ramon and Tarlé, Gregory and Vargas-Magaña, Mariana and Zhou, Zhimin},
abstractNote = {We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). The construction of the catalogues shown in this paper is part of a mock-comparison project within the Dark Energy Spectroscopic Instrument (DESI) collaboration. We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed N-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. We introduce cosmic-web-dependent corrections to modelling redshift-space distortions at the N-body level – both in the halo and galaxy distributions –, as well as a multi-scale approach for accurate assignment of halo properties. Using specific models of halo occupation distributions to populate halos, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. BAM generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display ~3%-10% accuracy in the multipoles of the power spectrum up to scales of k ~ 0.4 h-1Mpc. We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys.},
doi = {10.1051/0004-6361/202245618},
journal = {Astronomy and Astrophysics},
number = ,
volume = 673,
place = {United States},
year = {Thu May 18 00:00:00 EDT 2023},
month = {Thu May 18 00:00:00 EDT 2023}
}
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On the Prospect of Using the Maximum Circular Velocity of Halos to Encapsulate Assembly Bias in the Galaxy–Halo Connection
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Mapping the Three-dimensional Lyα Forest Large-scale Structure in Real and Redshift Space*
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COVMOS: A new Monte Carlo approach for galaxy clustering analysis
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The multidimensional dependence of halo bias in the eye of a machine: a tale of halo structure, assembly, and environment
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The DESI Survey Validation: Results from Visual Inspection of Bright Galaxies, Luminous Red Galaxies, and Emission-line Galaxies
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The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: a large sample of mock galaxy catalogues
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Scale dependence of halo and galaxy bias: Effects in real space
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Multitracer extension of the halo model: probing quenching and conformity in eBOSS
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The Dependence of Galaxy Clustering on Stellar-mass Assembly History for LRGs
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Tracing the cosmic web
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The luminosity-weighted or ‘marked’ correlation function
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The Dark Side of the Halo Occupation Distribution
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L-PICOLA: A parallel code for fast dark matter simulation
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The CAMELS Project: Cosmology and Astrophysics with Machine-learning Simulations
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The Large-Scale Structure of the Universe
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Beyond assembly bias: exploring secondary halo biases for cluster-size haloes
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Dark matter statistics for large galaxy catalogues: power spectra and covariance matrices
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Cosmological N -body simulations with suppressed variance
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AI-assisted superresolution cosmological simulations
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The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: 1000 multi-tracer mock catalogues with redshift evolution and systematics for galaxies and quasars of the final data release
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The effect of covariance estimator error on cosmological parameter constraints
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Optimal weighting in galaxy surveys: Application to redshift-space distortions
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Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument
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Reconstructing primordial density fluctuations – I. Method
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The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: mock galaxy catalogues for the BOSS Final Data Release
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Cosmological simulations for combined-probe analyses: covariance and neighbour-exclusion bias
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Target Selection and Validation of DESI Emission Line Galaxies
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The Excursion set Theory of halo mass Functions, halo Clustering, and halo Growth
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Why multitracer surveys beat cosmic variance
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Halo assembly bias and its effects on galaxy clustering
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Tracing the dark matter sheet in phase space: Tracing the dark matter sheet
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The Robotic Multiobject Focal Plane System of the Dark Energy Spectroscopic Instrument (DESI)
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The MIP ensemble simulation: local ensemble statistics in the Cosmic Web
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Overview of the DESI Milky Way Survey
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Stochastic Nonlinear Galaxy Biasing
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Target Selection and Validation of DESI Quasars
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Testing approximate predictions of displacements of cosmological dark matter halos
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Cosmic web type dependence of halo clustering
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An accurate tool for the fast generation of dark matter halo catalogues
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The pinocchio algorithm: pinpointing orbit-crossing collapsed hierarchical objects in a linear density field
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Clustering in real space and in redshift space
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MoLUSC: A Mock Local Universe Survey Constructor
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Comparing approximate methods for mock catalogues and covariance matrices – III: bispectrum
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The host dark matter haloes of [O II] emitters at 0.5 < z < 1.5
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The halo-model description of marked statistics
journal, December 2005

Fingers-of-God effect of infalling satellite galaxies
journal, November 2015

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Clustering properties of g -selected galaxies at z ∼ 0.8
journal, June 2016

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Estimating the power spectrum covariance matrix with fewer mock samples
journal, January 2016

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