Toward the measurement of neutrino masses: Performance of cosmic magnification with submillimeter galaxies

The phenomenon of magnification bias can induce a non-negligible angular correlation between two samples of galaxies with nonoverlapping redshift distributions. This signal is particularly clear when background submillimeter galaxies are used, and has been shown to constitute an independent cosmolog...

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Published inarXiv.org
Main Authors Cueli, M M, Cabo, S R, González-Nuevo, J, Bonavera, L, Lapi, A, Viel, M, Crespo, D, Casas, J M, Fernández-Fernández, R
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.06.2024
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Summary:The phenomenon of magnification bias can induce a non-negligible angular correlation between two samples of galaxies with nonoverlapping redshift distributions. This signal is particularly clear when background submillimeter galaxies are used, and has been shown to constitute an independent cosmological probe. This work extends prior studies on the submillimeter galaxy magnification bias to the massive neutrino scenario, with the aim being to assess its sensitivity as a cosmological observable to the sum of neutrino masses. The measurements of the angular cross-correlation function between moderate redshift GAMA galaxies and high-redshift submillimeter H-ATLAS galaxies are fit to the weak lensing prediction down to the arcmin scale. The signal is interpreted under the halo model, which is modified to accommodate massive neutrinos. We discuss the impact of the choice of cosmological parametrization on the sensitivity to neutrino masses. The currently available data on the magnification bias affecting submillimeter galaxies are sensitive to neutrino masses when a cosmological parametrization in terms of the primordial amplitude of the power spectrum \((A_s\)) is chosen over the local root mean square of smoothed linear density perturbations \((\sigma_8\)). A clear upper limit on the sum of neutrino masses can be derived if the value of \(A_s\) is either fixed or assigned a narrow Gaussian prior, a behavior that is robust against changes to the chosen value.
ISSN:2331-8422