Determining Dark Matter properties with a XENONnT/LZ signal and LHC-Run3 mono-jet searches
Sebastian Baum, Riccardo Catena, Jan Conrad, Katherine Freese and, Martin B. Krauss

TL;DR
This paper proposes a method to predict LHC Run 3 outcomes based on XENONnT dark matter signals, classifying models to determine dark matter properties and interactions, and analyzing potential experimental scenarios.
Contribution
It introduces a systematic classification approach for dark matter models to forecast LHC signals from XENONnT data, focusing on spin and interaction types.
Findings
Two mutually exclusive scenarios for dark matter interactions at LHC.
Detection or non-detection of mono-jet signals constrains dark matter spin and interaction type.
Spectral features at XENONnT inform LHC search strategies.
Abstract
We develop a method to forecast the outcome of the LHC Run 3 based on the hypothetical detection of signal events at XENONnT. Our method relies on a systematic classification of renormalisable single-mediator models for dark matter-quark interactions, and is valid for dark matter candidates of spin less than or equal to one. Applying our method to simulated data, we find that at the end of the LHC Run 3 only two mutually exclusive scenarios would be compatible with the detection of signal events at XENONnT. In a first scenario, the energy distribution of the signal events is featureless, as for canonical spin-independent interactions. In this case, if a mono-jet signal is detected at the LHC, dark matter must have spin 1/2 and interact with nucleons through a unique velocity-dependent operator. If a mono-jet signal is not detected, dark matter…
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