Detecting the radiative decay of the cosmic neutrino background with line-intensity mapping

TL;DR

This paper explores using line-intensity mapping to detect photons from neutrino decay in the cosmic background, potentially revealing new physics beyond the Standard Model with higher sensitivity than previous methods.

Contribution

It proposes a novel method to identify neutrino decay lines via LIM anisotropies and voxel intensity distribution, improving sensitivity to neutrino electromagnetic moments.

Findings

Sensitivity to transition moments of 10^{-12} to 10^{-8} μ_B with future LIM experiments.

Neutrino decay lines can be distinguished from astrophysical lines using anisotropy and voxel distribution.

Analytic form of the neutrino-density fluctuation PDF derived from simulations.

Abstract

We study the possibility to use line-intensity mapping (LIM) to seek photons from the radiative decay of neutrinos in the cosmic neutrino background. The Standard Model prediction for the rate for these decays is extremely small, but it can be enhanced if new physics increases the neutrino electromagnetic moments. The decay photons will appear as an interloper of astrophysical spectral lines. We propose that the neutrino-decay line can be identified with anisotropies in LIM clustering and also with the voxel intensity distribution. Ongoing and future LIM experiments will have -- depending on the neutrino hierarchy, transition and experiment considered -- a sensitivity to an effective electromagnetic transition moment $\sim 10^{-12}\, -\,10^{-8}\, (m_ic^2/{0.1 \rm eV})^{3/2}\mu_{\rm B}$, where $m_i$ is the mass of the decaying neutrino and $\mu_{\rm B}$ is the Bohr magneton. This will be significantly more sensitive than cosmic microwave background spectral distortions, and it will be competitive with stellar cooling studies. As a byproduct, we also report an analytic form of the one-point probability distribution function for neutrino-density fluctuations, obtained from the Quijote simulations using symbolic regression.