Characterizing unknown systematics in large scale structure surveys

Nishant Agarwal, Shirley Ho, Adam D. Myers, Hee Jong Seo, Ashley J. Ross, Neta Bahcall, Jonathan Brinkmann, Daniel J. Eisenstein, Demitri Muna, Nathalie Palanque-Delabrouille, Isabelle Pâris, Patrick Petitjean, Donald P. Schneider, Alina Streblyanska, Benjamin A. Weaver, Christophe Yèche

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

Original languageEnglish (US)
Article number007
JournalJournal of Cosmology and Astroparticle Physics
Volume2014
Issue number4
DOIs
StatePublished - Jan 1 2014

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cross correlation
contamination
estimates
galaxies
quasars
tracers
power spectra
universe
probes

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics

Cite this

Agarwal, N., Ho, S., Myers, A. D., Seo, H. J., Ross, A. J., Bahcall, N., ... Yèche, C. (2014). Characterizing unknown systematics in large scale structure surveys. Journal of Cosmology and Astroparticle Physics, 2014(4), [007]. https://doi.org/10.1088/1475-7516/2014/04/007
Agarwal, Nishant ; Ho, Shirley ; Myers, Adam D. ; Seo, Hee Jong ; Ross, Ashley J. ; Bahcall, Neta ; Brinkmann, Jonathan ; Eisenstein, Daniel J. ; Muna, Demitri ; Palanque-Delabrouille, Nathalie ; Pâris, Isabelle ; Petitjean, Patrick ; Schneider, Donald P. ; Streblyanska, Alina ; Weaver, Benjamin A. ; Yèche, Christophe. / Characterizing unknown systematics in large scale structure surveys. In: Journal of Cosmology and Astroparticle Physics. 2014 ; Vol. 2014, No. 4.
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Agarwal, N, Ho, S, Myers, AD, Seo, HJ, Ross, AJ, Bahcall, N, Brinkmann, J, Eisenstein, DJ, Muna, D, Palanque-Delabrouille, N, Pâris, I, Petitjean, P, Schneider, DP, Streblyanska, A, Weaver, BA & Yèche, C 2014, 'Characterizing unknown systematics in large scale structure surveys', Journal of Cosmology and Astroparticle Physics, vol. 2014, no. 4, 007. https://doi.org/10.1088/1475-7516/2014/04/007

Characterizing unknown systematics in large scale structure surveys. / Agarwal, Nishant; Ho, Shirley; Myers, Adam D.; Seo, Hee Jong; Ross, Ashley J.; Bahcall, Neta; Brinkmann, Jonathan; Eisenstein, Daniel J.; Muna, Demitri; Palanque-Delabrouille, Nathalie; Pâris, Isabelle; Petitjean, Patrick; Schneider, Donald P.; Streblyanska, Alina; Weaver, Benjamin A.; Yèche, Christophe.

In: Journal of Cosmology and Astroparticle Physics, Vol. 2014, No. 4, 007, 01.01.2014.

Research output: Contribution to journalArticle

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AU - Agarwal, Nishant

AU - Ho, Shirley

AU - Myers, Adam D.

AU - Seo, Hee Jong

AU - Ross, Ashley J.

AU - Bahcall, Neta

AU - Brinkmann, Jonathan

AU - Eisenstein, Daniel J.

AU - Muna, Demitri

AU - Palanque-Delabrouille, Nathalie

AU - Pâris, Isabelle

AU - Petitjean, Patrick

AU - Schneider, Donald P.

AU - Streblyanska, Alina

AU - Weaver, Benjamin A.

AU - Yèche, Christophe

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AB - Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

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