Journal
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 511, Issue 2, Pages 2170-2185Publisher
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stac147
Keywords
gravitational lensing: weak; methods: numerical; galaxies: distances and redshifts; large-scale structure of Universe
Categories
Funding
- European Research Council [681431]
- Beecroft Trust
- Agencia Nacional de Investigacion y Desarrollo (ANID) DOCTORADO BECAS CHILE [2016 - 72170279]
- 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
- Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico
- Ministerio da Ciencia, Tecnologia e Inovacao
- Deutsche Forschungsgemeinschaft
- 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 F'isica d'Altes Energies
- Lawrence Berkeley National Laboratory
- Ludwig-Maximilians Universitat Munchen
- associated Excellence Cluster Universe
- University of Michigan
- NSF's NOIRLab
- University of Nottingham
- Ohio State University
- University of Pennsylvania
- University of Portsmouth
- SLAC National Accelerator Laboratory
- Stanford University
- University of Sussex
- Texas AM University
- OzDES Membership Consortium
- National Science Foundation [AST-1138766, AST-1536171]
- MICINN [ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, MDM-2015-0509]
- ERDF funds from the European Union
- CERCA program of the Generalitat de Catalunya
- European Research Council under the European Union's Seventh Framework Program (FP7/2007-2013)
- ERC [240672, 291329, 306478]
- Brazilian Instituto Nacional de Ciencia e Tecnologia (INCT) do e-Universo (CNPq) [465376/2014-2]
- U.S. Department of Energy, Office of Science, Office of High Energy Physics [DE-AC02-07CH11359]
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In this paper, a new method called hyperrank is introduced for marginalizing over redshift distribution uncertainties, which can improve computational efficiency in weak lensing surveys. By using discrete samples and summary values, this method can effectively marginalize over various models of redshift distribution.
Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present hyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. In hyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we compare hyperrank to the common mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.
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