Viability of Boosted Light Dark Matter in a Two-Component Scenario
Arindam Basu, Amit Chakraborty, Nilanjana Kumar, Soumya Sadhukhan

TL;DR
This paper explores a two-component dark matter model with boosted light scalar and fermionic particles, analyzing their relic densities, interactions, and potential detectability within extended Higgs and vectorlike lepton frameworks.
Contribution
It introduces a novel two-component dark matter scenario with boosted light scalar DM and fermionic DM, including model extensions to satisfy experimental constraints and analyze relic densities.
Findings
Broader fermionic DM mass region due to annihilation channel
Scalar DM relic density increases with boost effects
Significant parameter space where total DM relic is fermion-dominated
Abstract
We study the boosted dark matter (BDM) scenario in a two-component model. We consider a neutrinophilic two-Higgs doublet model (2HDM), which consists of one extra Higgs doublet and a light right-handed neutrino. This model is extended with a light (~MeV) singlet scalar DM , which is stabilized under an extra dark symmetry and can only effectively annihilate through the CP even scalar . Although oblique parameters put tight constraints on the model, introduction of vectorlike leptons (VLL) can potentially salvage the issue. The vectorlike doublet and singlet are also stabilized through dark symmetry. The lightest vectorlike mass eigenstate (~GeV) is the 2nd DM component of the model. The fermion DM is restricted in a narrow mass region while a somewhat broader mass region is allowed for the scalar DM.…
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.
Taxonomy
TopicsDark Matter and Cosmic Phenomena · Particle physics theoretical and experimental studies · Computational Physics and Python Applications
