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 Chain Monte Carlo. The fit results from the MLE arc the least biased and their derived la error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAO signal. We show that incorrect assumptions in constructing the template, such as mismatches from the cosmology of the mocks 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.
How to cite this document
Chan, K. C. et al. BAO from angular clustering: optimization and mitigation of theoretical systematics. Monthly Notices Of The Royal Astronomical Society. Oxford: Oxford Univ Press, v. 480, n. 3, p. 3031-3051, 2018. Available at: <
http://hdl.handle.net/11449/185039>.
Sponsor
Spanish Ministerio de Economia y Competitividad
Juan de la Cierva fellowship
U.S. Department of Energy
U.S. National Science Foundation
Ministry of Science and Education of Spain
Science and Technology Facilities Council of the United Kingdom
Higher Education Funding Council for England
National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign
Kavli Institute of Cosmological Physics at the University of Chicago
Center for Cosmology and Astro-Particle Physics at the Ohio State University
Mitchell Institute for Fundamental Physics and Astronomy at Texas AM University
Financiadora de Estudos e Projetos
Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)
Ministerio da Ciencia, Tecnologia e Inovacao
Deutsche Forschungsgemeinschaft
National Science Foundation
Argonne National Laboratory
University of California at Santa Cruz
University of Cambridge
Centro de Investigaciones Energeticas
Medioambientales y Tecnologicas-Madrid
University of Chicago
University College London
DES-Brazil Consortium
University of Edinburgh
Eidgenossische Technische Hochschule (ETH) Zurich
Fermi National Accelerator Laboratory
University of Illinois at Urbana-Champaign
Institut de Ciencies de l'Espai (IEEC/CSIC)
Institut de Fisica d'Altes Energies
Lawrence Berkeley National Laboratory
Ludwig-Maximilians Universitat Munchen
associated Excellence Cluster Universe
University of Michigan
National Optical Astronomy Observatory
University of Nottingham
Ohio State University
University of Pennsylvania
University of Portsmouth
SLAC National Accelerator Laboratory
Stanford University
University of Sussex
Texas AM University
Grant number
Spanish Ministerio de Economia y Competitividad: ESP2013-48274-C3-1-P
National Science Foundation: AST-1138766