Constraints on Axions from Patchy Screening of the Cosmic Microwave Background

The resonant conversion of cosmic microwave background (CMB) photons into axions within large-scale structure induces an anisotropic spectral distortion in CMB temperature maps. Applying state-of-the-art foreground cleaning techniques to Planck CMB observations, we construct maps of axion-induced "patchy screening" of the CMB. We cross-correlate these maps with data from the unWISE galaxy survey and find no evidence of axions. We constrain the axion-photon coupling, g_{aγγ}≲2×10^{-12} GeV^{-1}, at the 95% confidence level for axion masses in the range 10^{-13} eV≲m_{a}≲10^{-12} eV. These constraints are competitive with the tightest astrophysical axion limits in this mass range and are inferred from robust population-level statistics, which makes them complementary to existing searches that rely on modeling of individual systems.

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Publication Details

Journal
Physical Review Letters
Published
2025-02-26
DOI
https://doi.org/10.1103/physrevlett.134.081001
Citations
5
Primary Topic
Dark Matter and Cosmic Phenomena
Type
article
Field-Weighted Citation Impact
9.35

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Constraints on Axions from Patchy Screening of the Cosmic Microwave Background

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Constraints on Axions from Patchy Screening of the Cosmic Microwave Background

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article en
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Abstract

The resonant conversion of cosmic microwave background (CMB) photons into axions within large-scale structure induces an anisotropic spectral distortion in CMB temperature maps. Applying state-of-the-art foreground cleaning techniques to Planck CMB observations, we construct maps of axion-induced "patchy screening" of the CMB. We cross-correlate these maps with data from the unWISE galaxy survey and find no evidence of axions. We constrain the axion-photon coupling, g_{aγγ}≲2×10^{-12} GeV^{-1}, at the 95% confidence level for axion masses in the range 10^{-13} eV≲m_{a}≲10^{-12} eV. These constraints are competitive with the tightest astrophysical axion limits in this mass range and are inferred from robust population-level statistics, which makes them complementary to existing searches that rely on modeling of individual systems.

Physical Review LettersVol. 134(8)
Perimeter Institute (CA), York University (CA), Flatiron Health (United States) (US), Flatiron Institute (US), Columbia University (US)
National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Simons Foundation, New York State Energy Research and Development Authority, University of Texas at Austin, Government of Canada, Ontario Ministry of Research, Innovation and Science, National Institutes of Health, Horizon 2020 Framework Programme, Natural Sciences and Engineering Research Council of Canada, H2020 European Research Council
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Dark Matter and Cosmic Phenomena
9.35
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