Harnessing the Population Statistics of Subhalos to Search for Annihilating Dark Matter
Jean J. Somalwar, Laura J. Chang, Siddharth Mishra-Sharma, Mariangela, Lisanti

TL;DR
This paper introduces a novel statistical method to detect annihilating dark matter by analyzing the collective gamma-ray emission from subhalos in the Milky Way, even when individual sources are too faint to resolve.
Contribution
It presents a new population-based approach that leverages subhalo spatial and mass distributions to identify dark matter signals in gamma-ray data, complementing existing source-based searches.
Findings
Simulated data show collective subhalo emission affects photon statistics.
The method can detect dark matter signals despite unresolved astrophysical backgrounds.
Potential application to Fermi-LAT and CTA data for dark matter searches.
Abstract
The Milky Way's dark matter halo is expected to host numerous low-mass subhalos with no detectable associated stellar component. Such subhalos are invisible unless their dark matter annihilates to visible states such as photons. One of the established methods for identifying candidate subhalos is to search for individual unassociated gamma-ray sources with properties consistent with the dark matter expectation. However, robustly ruling out an astrophysical origin for any such candidate is challenging. In this work, we present a complementary approach that harnesses information about the entire population of subhalos---such as their spatial and mass distribution in the Galaxy---to search for a signal of annihilating dark matter. Using simulated data, we show that the collective emission from subhalos can imprint itself in a unique way on the statistics of observed photons, even when…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
