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    Dark Energy Survey Year 3 results: Marginalization over redshift distribution uncertainties using ranking of discrete realizations

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    Author
    DES Collaboration
    Affiliation
    Department of Astronomy/Steward Observatory, University of Arizona
    Issue Date
    2022
    Keywords
    Galaxies: distances and redshifts
    Gravitational lensing: weak
    Large-scale structure of Universe
    Methods: numerical
    
    Metadata
    Show full item record
    Publisher
    Oxford University Press
    Citation
    Cordero, J. P., Harrison, I., Rollins, R. P., Bernstein, G. M., Bridle, S. L., Alarcon, A., Alves, O., Amon, A., Andrade-Oliveira, F., Camacho, H., Campos, A., Choi, A., Derose, J., Dodelson, S., Eckert, K., Eifler, T. F., Everett, S., Fang, X., Friedrich, O., … Varga, T. N. (2022). Dark Energy Survey Year 3 results: Marginalization over redshift distribution uncertainties using ranking of discrete realizations. Monthly Notices of the Royal Astronomical Society.
    Journal
    Monthly Notices of the Royal Astronomical Society
    Rights
    Copyright © 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    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. © 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society.
    Note
    Immediate access
    ISSN
    0035-8711
    DOI
    10.1093/mnras/stac147
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1093/mnras/stac147
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    UA Faculty Publications

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