A Bayesian analysis of sneutrino DM in the NMSSM with Type-I seesaw mechanism
Junjie Cao, Jie Li, Yusi Pan, Liangliang Shang, Yuanfang Yue, Di Zhang

TL;DR
This paper explores sneutrino dark matter within an extended NMSSM framework incorporating a Type-I seesaw mechanism, analyzing its viability through comprehensive statistical scans and comparing predictions with experimental data.
Contribution
It introduces a Bayesian and frequentist analysis of sneutrino DM in the NMSSM with Type-I seesaw, highlighting viable parameter regions and naturalness in Higgsino mass predictions.
Findings
Sneutrino DM scenarios are viable over broad parameter regions.
Higgsino mass can be below 250 GeV, aligning with naturalness.
DM co-annihilates with Higgsinos, suppressing scattering rates.
Abstract
In the Next-to-Minimal Supersymmetric Standard Model (NMSSM) with extra heavy neutrino superfields, neutrino may acquire its mass via a seesaw mechanism and sneutrino may act as a viable dark matter (DM) candidate. Given the strong tension between the naturalness for boson mass and the DM direct detection experiments for customary neutralino DM candidate, we augment the NMSSM with Type-I seesaw mechanism, which is the simplest extension of the theory to predict neutrino mass, and study the scenarios of sneutrino DM. We construct likelihood function with LHC Higgs data, B-physics measurements, DM relic density and its direct and indirect search limits, and perform a comprehensive scan over the parameter space of the theory by Nested Sampling method. We adopt both Bayesian and frequentist statistical quantities to illustrate the favored parameter space of the scenarios, the 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.
