BAO from angular clustering: Optimization and mitigation of theoretical systematics

DES Collaboration

doi:10.1093/MNRAS/STY2036

BAO from angular clustering: Optimization and mitigation of theoretical systematics

DES Collaboration

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Abstract

We study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov ChainMonte Carlo. The fit results from the MLE are the least biased and their derived 1σ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAOsignal. We showthat incorrect assumptions in constructing the template, such as mismatches from the cosmology of themocks or the underlying photo-z errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced.

Original language	English
Pages (from-to)	3031-3051
Number of pages	21
Journal	Monthly Notices of the Royal Astronomical Society
Volume	480
Issue number	3
DOIs	https://doi.org/10.1093/MNRAS/STY2036
State	Published - 2018

Funding

KCC acknowledges the support from the Spanish Ministerio de Economia y Competitividad grant ESP2013-48274-C3-1-P and the Juan de la Cierva fellowship. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundac¸ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desen-volvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovac¸ão, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the DES. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. KCC acknowledges the support from the Spanish Ministerio de Economia y Competitividad grant ESP2013-48274-C3-1-P and the Juan de la Cierva fellowship. We are grateful for the extraordinary contributions of our CTIO colleagues and the DECam Construction, Commissioning and Science Verification teams in achieving the excellent instrument and telescope conditions that have made this work possible. The success of this project also relies critically on the expertise and dedication of the DES Data Management group. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology FacilitiesCouncil of theUnitedKingdom, theHigher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundaç ão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovaç ão, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the DES. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Enérgeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciències de l'Espai (IEEC/CSIC), the Institut de Física d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University. This paper has gone through internal review by the DES collaboration. The DES publication number for this article is DES-2017-0306. The Fermilab preprint number is FERMILAB-PUB-17-590. We thank the anonymous referee for thoroughly reviewing this paper and providing constructive comments.

Funders	Funder number
Conselho Nacional de Desen-volvimento Científico e Tecnológico
Ministério da Ciência
Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University
National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
Science and Technology Facilities Council of the United Kingdom
National Science Foundation	AST-1138766
U.S. Department of Energy
Directorate for Mathematical and Physical Sciences	1138766
Stanford University
Argonne National Laboratory
Ohio State University
University of Chicago
University of Michigan
Higher Education Funding Council for England
Deutsche Forschungsgemeinschaft
Ministerio de Economía y Competitividad	ESP2013-48274-C3-1-P
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Financiadora de Estudos e Projetos
Ministry of Education and Science of Ukraine
National Science Foundation

Keywords

Cosmology: observations
Large-scale structure of Universe

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10.1093/MNRAS/STY2036

Cite this

@article{5004466a19b34cee81afecf3cac4fdce,

title = "BAO from angular clustering: Optimization and mitigation of theoretical systematics",

abstract = "We study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov ChainMonte Carlo. The fit results from the MLE are the least biased and their derived 1σ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAOsignal. We showthat incorrect assumptions in constructing the template, such as mismatches from the cosmology of themocks or the underlying photo-z errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced.",

keywords = "Cosmology: observations, Large-scale structure of Universe",

author = "{DES Collaboration} and Chan, {K. C.} and M. Crocce and Ross, {A. J.} and S. Avila and J. Elvin-Poole and M. Manera and Percival, {W. J.} and R. Rosenfeld and Abbott, {T. M.C.} and Abdalla, {F. B.} and S. Allam and E. Bertin and D. Brooks and Burke, {D. L.} and Rosell, {A. Carnero} and Kind, {M. Carrasco} and J. Carretero and Castander, {F. J.} and Cunha, {C. E.} and D'Andrea, {C. B.} and {da Costa}, {L. N.} and C. Davis and {De Vicente}, J. and Eifler, {T. F.} and J. Estrada and B. Flaugher and P. Fosalba and J. Frieman and J. Garc{\'i}a-Bellido and E. Gaztanaga and Gerdes, {D. W.} and D. Gruen and Gruendl, {R. A.} and J. Gschwend and G. Gutierrez and Hartley, {W. G.} and K. Honscheid and B. Hoyle and James, {D. J.} and E. Krause and K. Kuehn and O. Lahav and M. Lima and M. March and F. Menanteau and Miller, {C. J.} and R. Miquel and Plazas, {A. A.} and K. Reil and E. Suchyta",

note = "Publisher Copyright: {\textcopyright} 2018 The Author(s).",

year = "2018",

doi = "10.1093/MNRAS/STY2036",

language = "English",

volume = "480",

pages = "3031--3051",

journal = "Monthly Notices of the Royal Astronomical Society",

issn = "0035-8711",

publisher = "Oxford University Press",

number = "3",

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TY - JOUR

T1 - BAO from angular clustering

T2 - Optimization and mitigation of theoretical systematics

AU - DES Collaboration

AU - Chan, K. C.

AU - Crocce, M.

AU - Ross, A. J.

AU - Avila, S.

AU - Elvin-Poole, J.

AU - Manera, M.

AU - Percival, W. J.

AU - Rosenfeld, R.

AU - Abbott, T. M.C.

AU - Abdalla, F. B.

AU - Allam, S.

AU - Bertin, E.

AU - Brooks, D.

AU - Burke, D. L.

AU - Rosell, A. Carnero

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Castander, F. J.

AU - Cunha, C. E.

AU - D'Andrea, C. B.

AU - da Costa, L. N.

AU - Davis, C.

AU - De Vicente, J.

AU - Eifler, T. F.

AU - Estrada, J.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gaztanaga, E.

AU - Gerdes, D. W.

AU - Gruen, D.

AU - Gruendl, R. A.

AU - Gschwend, J.

AU - Gutierrez, G.

AU - Hartley, W. G.

AU - Honscheid, K.

AU - Hoyle, B.

AU - James, D. J.

AU - Krause, E.

AU - Kuehn, K.

AU - Lahav, O.

AU - Lima, M.

AU - March, M.

AU - Menanteau, F.

AU - Miller, C. J.

AU - Miquel, R.

AU - Plazas, A. A.

AU - Reil, K.

AU - Suchyta, E.

PY - 2018

Y1 - 2018

N2 - We study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov ChainMonte Carlo. The fit results from the MLE are the least biased and their derived 1σ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAOsignal. We showthat incorrect assumptions in constructing the template, such as mismatches from the cosmology of themocks or the underlying photo-z errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced.

AB - We study the methodology and potential theoretical systematics of measuring baryon acoustic oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 data set using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov ChainMonte Carlo. The fit results from the MLE are the least biased and their derived 1σ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAOsignal. We showthat incorrect assumptions in constructing the template, such as mismatches from the cosmology of themocks or the underlying photo-z errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalogue. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced.

KW - Cosmology: observations

KW - Large-scale structure of Universe

UR - http://www.scopus.com/inward/record.url?scp=85055143485&partnerID=8YFLogxK

U2 - 10.1093/MNRAS/STY2036

DO - 10.1093/MNRAS/STY2036

M3 - Article

AN - SCOPUS:85055143485

SN - 0035-8711

VL - 480

SP - 3031

EP - 3051

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

IS - 3

ER -

BAO from angular clustering: Optimization and mitigation of theoretical systematics

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