Searching for dark matter subhalos in the Fermi-LAT catalog with Bayesian neural networks

被引:0
作者
Butter, Anja [1 ,2 ]
Kraemer, Michael [3 ]
Manconi, Silvia [3 ,4 ]
Nippel, Kathrin [3 ]
机构
[1] Univ Paris Cite, Sorbonne Univ, LPNHE, CNRS,N2P3, Paris, France
[2] Heidelberg Univ, Inst Theoret Phys, Heidelberg, Germany
[3] Rhein Westfal TH Aachen, Inst Theoret Particle Phys & Cosmol, D-52056 Aachen, Germany
[4] USMB, CNRS, Lab Annecy Vieux Phys Theor LAPTh, F-74940 Annecy, France
来源
JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS | 2023年 / 07期
关键词
dark matter simulations; gamma ray experiments; gamma ray theory; Machine learning;
D O I
10.1088/1475-7516/2023/07/033
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
About a third of the gamma -ray sources detected by the Fermi Large Area Telescope (Fermi-LAT) remain unidentified, and some of these could be exotic objects such as dark matter subhalos. We present a search for these sources using Bayesian neural network classification methods applied to the latest 4FGL-DR3 Fermi-LAT catalog. We first simulate the gamma-ray properties of dark matter subhalos using models from N-body simulations and semi-analytical approaches to the subhalo distribution. We then assess the detectability of this sample in the 4FGL-DR3 catalog using the Fermi-LAT analysis tools. We train our Bayesian neural network to identify candidate dark matter subhalos among the unidentified sources in the 4FGL-DR3 catalog. Our results allow us to derive conservative bounds on the dark matter annihilation cross section by excluding unidentified sources classified as astrophysical-like by our networks. We estimate the number of candidate dark matter subhalos for different dark matter masses and provide a publicly available list for further investigation. Our bounds on the dark matter annihilation cross section are comparable to previous results and become particularly competitive at high dark matter masses.
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页数:32
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