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dc.contributor.authorBoruah, S.S.
dc.contributor.authorLavaux, G.
dc.contributor.authorHudson, M.J.
dc.date.accessioned2024-01-24T05:45:04Z
dc.date.available2024-01-24T05:45:04Z
dc.date.issued2022-10-21
dc.identifier.citationBoruah, S. S., Lavaux, G., & Hudson, M. J. (2022). Bayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias. Monthly Notices of the Royal Astronomical Society, 517(3), 4529-4543.
dc.identifier.issn0035-8711
dc.identifier.doi10.1093/mnras/stac2985
dc.identifier.urihttp://hdl.handle.net/10150/670702
dc.description.abstractWe present a Bayesian velocity field reconstruction algorithm that performs the reconstruction of the mass density field using only peculiar velocity data. Our method consistently accounts for the inhomogeneous Malmquist (IHM) bias using analytical integration along the line of sight. By testing our method on a simulation, we show that our method gives an unbiased reconstruction of the velocity field. We show that not accounting for the IHM bias can lead to significant biases in the Bayesian reconstructions. We applied our method to a peculiar velocity data set consisting of the SFI++ and 2MTF Tully-Fisher catalogues and the A2 supernovae compilation, thus obtaining a novel velocity reconstruction in the local Universe. Our velocity reconstructions have a cosmological power spectrum consistent with the theoretical expectation. Furthermore, we obtain a full description of the uncertainties on reconstruction through samples of the posterior distribution. We validate our velocity reconstruction of the local Universe by comparing it to an independent reconstruction using the 2M++ galaxy catalogue, obtaining good agreement between the two reconstructions. Using Bayesian model comparison, we find that our velocity model performs better than the adaptive kernel smoothed velocity with the same peculiar velocity data. However, our velocity model does not perform as well as the velocity reconstruction from the 2M++ galaxy catalogue, due to the sparse and noisy nature of the peculiar velocity tracer samples. The method presented here provides a way to include peculiar velocity data in initial condition reconstruction frameworks. © 2022 The Author(s).
dc.language.isoen
dc.publisherOxford University Press
dc.rights© 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectcosmology: observations
dc.subjectgalaxies: kinematics and dynamics
dc.subjectlarge-scale structure of Universe
dc.titleBayesian reconstruction of dark matter distribution from peculiar velocities: accounting for inhomogeneous Malmquist bias
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Astronomy and Steward Observatory, University of Arizona
dc.identifier.journalMonthly Notices of the Royal Astronomical Society
dc.description.noteImmediate access
dc.description.collectioninformationThis 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.
dc.eprint.versionFinal published version
dc.source.journaltitleMonthly Notices of the Royal Astronomical Society
refterms.dateFOA2024-01-24T05:45:04Z


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