Probing Freeze-In Dark Matter via a Spin-2 Portal at the LHC with Vector Boson Fusion and Machine Learning

TL;DR

This paper explores the collider signatures of freeze-in dark matter mediated by a spin-2 particle at the LHC, using machine learning to improve detection sensitivity and connecting cosmological relic abundance with collider observables.

Contribution

It introduces a novel collider search strategy for feebly interacting dark matter via a spin-2 portal, utilizing machine learning to enhance sensitivity at the LHC.

Findings

Collider searches can probe significant parts of the freeze-in parameter space.

Machine learning algorithms improve detection sensitivity for feebly interacting mediators.

High-luminosity LHC can test cosmologically relevant dark matter scenarios.

Abstract

The persistent absence of signals in traditional dark matter searches has intensified interest in scenarios beyond the canonical weakly interacting massive particle paradigm. In this work, we investigate the collider phenomenology of feebly interacting dark matter produced via the freeze-in mechanism through a spin-2 portal. We consider a framework in which a massive graviton-like mediator couples minimally and universally to the energy--momentum tensor of both the Standard Model (SM) and the dark sector. Such interactions arise naturally in extra-dimensional constructions and effective theories of gravity, providing a theoretically well-motivated and predictive setup. We systematically connect early-Universe cosmology with collider observables by identifying regions of parameter space consistent with freeze-in conditions and the observed dark matter relic abundance, and examining their testability at the Large Hadron Collider (LHC). Focusing on bosonic fusion production channels, which are particularly sensitive to spin-2 interactions, we analyze invisible mediator decay signatures and assess current and projected experimental sensitivities. To enhance sensitivity in this challenging regime of feeble couplings, we develop a search strategy based on machine-learning algorithms. Our results demonstrate that collider searches can probe substantial regions of the cosmologically viable freeze-in parameter space, highlighting the high-luminosity LHC as a powerful laboratory for feebly interacting dark sectors. This study establishes a concrete and complementary pathway to test freeze-in dark matter scenarios through spin-2 portals, thereby bridging gravitationally motivated new physics, cosmology, and high-energy collider experiments.