Dark Energy Survey Year 3 results: Marginalization over redshift distribution uncertainties using ranking of discrete realizations
Entity
UAM. Departamento de Física TeóricaPublisher
Royal Astronomical Society; Oxford University PressDate
2022-01-12Citation
10.1093/mnras/stac147
Monthly Notices of the Royal Astronomical Society 511.2 (2022): 2170-2185
ISSN
0035-8711 (print); 1365-2966 (online)DOI
10.1093/mnras/stac147Project
Gobierno de España. ESP2017-89838-C3-1-R; Gobierno de España. PGC2018-094773-B-C33; Gobierno de España. PGC2018-102021-B-I00; Gobierno de España. SEV-2016-0588; Gobierno de España. SEV-2016-0597; info:eu-repo/grantAgreement/EC/FP7/240672/EU//COGS; info:eu-repo/grantAgreement/EC/FP7/291329/EU//TESTDE; info:eu-repo/grantAgreement/EC/FP7/306478/EU//COSMICDAWNEditor's Version
https://doi.org/10.1093/mnras/stac147Subjects
Gravitational Lensing: Weak; Methods: Numerical; Galaxies: Distances and Redshifts; Large-Scale Structure of Universe; FísicaNote
Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiereRights
© 2022 The Author(s)Abstract
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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Google Scholar:Cordero, Juan P.
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García-Bellido Capdevila, Juan
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DES Collaboration
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