Casting a Wide Signal Net with Future Direct Dark Matter Detection Experiments
pith:QCF5H5YM Add to your LaTeX paper
What is a Pith Number?\usepackage{pith}
\pithnumber{QCF5H5YM}
Prints a linked pith:QCF5H5YM badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more
read the original abstract
As dark matter (DM) direct detection experiments continue to improve their sensitivity they will inevitably encounter an irreducible background arising from coherent neutrino scattering. This so-called "neutrino floor" may significantly reduce the sensitivity of an experiment to DM-nuclei interactions, particularly if the recoil spectrum of the neutrino background is approximately degenerate with the DM signal. This occurs for the conventionally considered spin-independent (SI) or spin-dependent (SD) interactions. In such case, an increase in the experiment's exposure by multiple orders of magnitude may not yield any significant increase in sensitivity. The typically considered SI and SD interactions, however, do not adequately reflect the whole landscape of the well-motivated DM models, which includes other interactions. Since particle DM has not been detected yet in laboratories, it is essential to understand and maximize the detection capabilities for a broad variety of possible models and signatures. In this work we explore the impact of the background arising from various neutrino sources on the discovery potential of a DM signal for a large class of viable DM-nucleus interactions and several potential futuristic experimental settings, with different target elements. For some momentum suppressed cross sections, large DM particle masses and heavier targets, we find that there is no suppression of the discovery limits due to neutrino backgrounds. Further, we explicitly demonstrate that inelastic scattering, which could appear in models with multicomponent dark sectors, would help to lift the signal degeneracy associated with the neutrino floor. This study could assist with mapping out the optimal DM detection strategy for the next generation of experiments.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Machine Learning in the 2HDM2S model for Dark Matter
A 2HDM extended by two real scalar singlets is scanned with evolutionary strategies to locate regions satisfying vacuum, unitarity, oblique-parameter, collider and dark-matter constraints.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.