BAO from angular clustering: Optimization and mitigation of theoretical systematics

DES Collaboration

Research output: Contribution to journalArticle

4 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)3031-3051
Number of pages21
JournalMonthly Notices of the Royal Astronomical Society
Volume480
Issue number3
DOIs
StatePublished - Jan 1 2018

Fingerprint

baryons
mitigation
acoustics
estimators
oscillation
oscillations
optimization
matrix
expansion
cosmology
dark energy
angular correlation
normal density functions
methodology
catalogs
templates
energy
shift
profiles
parameter

Keywords

  • Cosmology: observations
  • Large-scale structure of Universe

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

BAO from angular clustering : Optimization and mitigation of theoretical systematics. / DES Collaboration.

In: Monthly Notices of the Royal Astronomical Society, Vol. 480, No. 3, 01.01.2018, p. 3031-3051.

Research output: Contribution to journalArticle

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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.",
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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 A. Roodman",
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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 - Roodman, A.

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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

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U2 - 10.1093/MNRAS/STY2036

DO - 10.1093/MNRAS/STY2036

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JO - Monthly Notices of the Royal Astronomical Society

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