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Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses

Krause, E. and Eifler, T. F. (2017) Dark Energy Survey Year 1 Results: Multi-Probe Methodology and Simulated Likelihood Analyses. . (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20180913-153252460

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Abstract

We present the methodology for and detail the implementation of the Dark Energy Survey (DES) 3x2pt DES Year 1 (Y1) analysis, which combines configuration-space two-point statistics from three different cosmological probes: cosmic shear, galaxy-galaxy lensing, and galaxy clustering, using data from the first year of DES observations. We have developed two independent modeling pipelines and describe the code validation process. We derive expressions for analytical real-space multi-probe covariances, and describe their validation with numerical simulations. We stress-test the inference pipelines in simulated likelihood analyses that vary 6-7 cosmology parameters plus 20 nuisance parameters and precisely resemble the analysis to be presented in the DES 3x2pt analysis paper, using a variety of simulated input data vectors with varying assumptions. We find that any disagreement between pipelines leads to changes in assigned likelihood Δχ^2 ≤ 0.045 with respect to the statistical error of the DES Y1 data vector. We also find that angular binning and survey mask do not impact our analytic covariance at a significant level. We determine lower bounds on scales used for analysis of galaxy clustering (8 Mpc h^(−1)) and galaxy-galaxy lensing (12 Mpc h^(−1)) such that the impact of modeling uncertainties in the non-linear regime is well below statistical errors, and show that our analysis choices are robust against a variety of systematics. These tests demonstrate that we have a robust analysis pipeline that yields unbiased cosmological parameter inferences for the flagship 3x2pt DES Y1 analysis. We emphasize that the level of independent code development and subsequent code comparison as demonstrated in this paper is necessary to produce credible constraints from increasingly complex multi-probe analyses of current data.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://arxiv.org/abs/1706.09359arXivDiscussion Paper
Additional Information:The figures in this work are produced with plotting routines from matplotlib [117], ChainConsumer[118], and Emmanuel Schaan. EK was supported by a Kavli Fellowship at Stanford University. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration and is supported by NASA ROSES ATP 16-ATP16-0084 grant and by NASA ROSES 16- ADAP16-0116. OF was supported by SFB-Transregio 33 'The Dark Universe' of the Deutsche Forschungsgemeinschaft (DFG) and by the DFG Cluster of Excellence 'Origin and Structure of the Universe'. EK, TE, and RHW thank the Kavli Institute for Theoretical Physics, supported in part by the National Science Foundation under Grant No. NSF PHY-1125915, for hospitality while this work was completed. 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, Fundação 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 Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energéticas, 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, Texas A&M University, and the OzDES Membership Consortium. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2015-71825, ESP2015-88861, FPA2015-68048, SEV- 2012-0234, SEV-2012-0249, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. YO acknowledges funding from the Natural Sciences and Engineering Research Council of Canada, Canadian Institute for Advanced Research, and Canada Research Chairs program. N.K. acknowledges support from FAPESP through grant 2015/20863-9. Some of the computations in this paper were made on the supercomputer Guillimin from McGill University, managed by Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the Canada Foundation for Innovation (CFI), the ministère de l'Économie, de la science et de l'innovation du Québec (MESI) and the Fonds de recherche du Québec - Nature et technologies (FRQ-NT). Parts of this research is carried out as part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications.
Funders:
Funding AgencyGrant Number
Stanford UniversityUNSPECIFIED
NASA/JPL/CaltechUNSPECIFIED
NASA16-ATP16-0084
NASA16-ADAP16-0116
Deutsche Forschungsgemeinschaft (DFG)SFB-Transregio 33
NSFPHY-1125915
Department of Energy (DOE)UNSPECIFIED
Ministerio de Educación y Ciencia (MEC)UNSPECIFIED
Science and Technology Facilities Council (STFC)UNSPECIFIED
Higher Education Funding Council for EnglandUNSPECIFIED
University of Illinois Urbana-ChampaignUNSPECIFIED
University of ChicagoUNSPECIFIED
Ohio State UniversityUNSPECIFIED
Texas A&M UniversityUNSPECIFIED
Financiadora de Estudos e Projetos (FINEP)UNSPECIFIED
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)UNSPECIFIED
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UNSPECIFIED
Ministério da Ciência, Tecnologia e InovaçãoUNSPECIFIED
Argonne National LaboratoryUNSPECIFIED
University of California, Santa CruzUNSPECIFIED
University of CambridgeUNSPECIFIED
Centro de Investigaciones Energéticas, Medioambientales y TecnológicasUNSPECIFIED
University College LondonUNSPECIFIED
DES-Brazil ConsortiumUNSPECIFIED
University of EdinburghUNSPECIFIED
ETH Zurich UNSPECIFIED
Fermi National Accelerator LaboratoryUNSPECIFIED
Institut de Ciències de l’EspaiUNSPECIFIED
Institut de Física d’Altes EnergiesUNSPECIFIED
Lawrence Berkeley National LaboratoryUNSPECIFIED
Ludwig-Maximilians Universität MünchenUNSPECIFIED
Excellence Cluster UniverseUNSPECIFIED
University of MichiganUNSPECIFIED
National Optical Astronomy ObservatoryUNSPECIFIED
University of NottinghamUNSPECIFIED
University of PennsylvaniaUNSPECIFIED
University of PortsmouthUNSPECIFIED
Stanford Linear Accelerator CenterUNSPECIFIED
University of SussexUNSPECIFIED
OzDES Membership ConsortiumUNSPECIFIED
NSFAST-1138766
Ministerio de Economía, Industria y Competitividad (MINECO)AYA2015-71825
Ministerio de Economía, Industria y Competitividad (MINECO)ESP2015-88861
Ministerio de Economía, Industria y Competitividad (MINECO)FPA2015-68048
Centro de Excelencia Severo OchoaSEV-2012-0234
Centro de Excelencia Severo OchoaSEV-2012-0249
Ministerio de Economía, Industria y Competitividad (MINECO)MDM-2015-0509
European Regional Development FundUNSPECIFIED
Generalitat de CatalunyaUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Canadian Institute for Advanced Research (CIFAR)UNSPECIFIED
Canada Research Chairs ProgramUNSPECIFIED
Fundação de Amparo à Pesquisa do Estado de Sao Paulo (FAPESP)2015/20863-9
Canada Foundation for Innovation (CFI)UNSPECIFIED
Ministère de l'Économie, de la science et de l'innovation du Québec (MESI)UNSPECIFIED
Fonds de recherche du Québec - Nature et technologies (FRQ-NT)UNSPECIFIED
NSFOCI-0725070
NSFACI-1238993
State of IllinoisUNSPECIFIED
Record Number:CaltechAUTHORS:20180913-153252460
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180913-153252460
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:89629
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:13 Sep 2018 23:01
Last Modified:13 Sep 2018 23:16

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