English

Constraining the Secluded and Catalyzed Annihilation Dark Matter with Fermi-LAT and Planck Data

High Energy Physics - Phenomenology 2025-05-20 v2 Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Experiment

Abstract

We propose a dark matter (DM) model with a complex scalar charged under a hidden gauge symmetry, denoted as U(1)DU(1)_D. The scalar field is the DM candidate while the U(1)DU(1)_D gauge field AA' plays the role of a mediator, which connects the dark sector to the standard model (SM) sector via a tiny kinetic mixing. We find that both the secluded and catalyzed annihilation scenarios can be realized in this model. The phenomenology of DM, including relic density, indirect detection (Fermi-LAT), and CMB (Planck) constraints, is discussed. We also extend our discussion to DM with other spins, including Dirac fermion and vector boson. Our analysis is carried out in two models, denoted as U(1)D×U(1)YU(1)_D \times U(1)_Y and U(1)D×U(1)LμLτU(1)_D \times U(1)_{L_\mu-L_\tau}, with the former corresponding to AA' kinetically mixing with the U(1)YU(1)_Y gauge field BB and the latter corresponding to AA' mixing with the U(1)LμLτU(1)_{L_\mu-L_\tau} gauge field ZZ'. We find that, in previous studies, the indirect detection limits were overly restrictive because they only considered the simplified 2DM2SM2\mathrm{DM} \to 2\mathrm{SM} annihilation channel. In contrast, by performing a complete calculation of the gamma-ray and CMB constraints from the process 2DM2A4SM2\mathrm{DM} \to 2A' \to 4\mathrm{SM} in the models we consider, we observe weaker constraints in both the U(1)D×U(1)YU(1)_D \times U(1)_Y and U(1)D×U(1)LμLτU(1)_D \times U(1)_{L_\mu-L_\tau} models, with the U(1)D×U(1)LμLτU(1)_D \times U(1)_{L_\mu-L_\tau} model being subject to the weakest constraints overall since it involves less hadronic decay processes.

Keywords

Cite

@article{arxiv.2501.09647,
  title  = {Constraining the Secluded and Catalyzed Annihilation Dark Matter with Fermi-LAT and Planck Data},
  author = {Yu-Hang Su and Chengfeng Cai and Hong-Hao Zhang},
  journal= {arXiv preprint arXiv:2501.09647},
  year   = {2025}
}