Dark Matter in the Alternative Left Right Model
Mariana Frank, Chayan Majumdar, Poulose Poulose, Supriya Senapati,, Urjit A. Yajnik

TL;DR
This paper explores the natural emergence of dark matter candidates in the Alternative Left-Right Model, analyzing their detection prospects at the LHC and their compatibility with relic density and detection bounds.
Contribution
It introduces a novel dark matter framework within the Alternative Left-Right Model, including fermionic and bosonic candidates, and assesses their experimental signatures and parameter space constraints.
Findings
Dark matter candidates can be fermionic or bosonic.
LHC signals for bosonic candidates involve exotic d' quarks.
Fermionic candidates suggest charged Higgs bosons in the TeV range.
Abstract
The Alternative Left-Right Model is an attractive variation of the usual Left-Right Symmetric Model because it avoids flavour-changing neutral currents, thus allowing the additional Higgs bosons in the model to be light. We show here that the model predicts several dark matter candidates naturally, through introduction of an -parity similar to the one in supersymmetry, under which some of the new particles are odd, while all the SM particles are even. Dark matter candidates can be fermionic or bosonic. We present a comprehensive investigation of all possibilities. We analyze and restrict the parameter space where relic density, direct and indirect detection bounds are satisfied, and investigate the possibility of observing fermionic and bosonic dark matter signals at the LHC. Both the bosonic and fermionic candidates provide promising signals, the first in LHC at 300 fb, the…
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
