Probing dark QCD sector through the Higgs portal with machine learning at the LHC

We study confining dark sectors where the lightest hadrons are glueballs. Such models can provide viable dark matter candidates and appear in some neutral naturalness scenarios. In this work, we introduce a new phenomenological model of dark glueball hadronization inspired by the Lund string model. This enables us to make the most physically-motivated predictions for dark glueball phenomenology at the LHC to date. Our model approximately reproduces the expected thermal distribution of hadron species as an emergent consequence of hadronization dynamics. The ability to predict the production of glueball states heavier than the lightest species significantly expands the reach of long-lived glueball searches in MATHUSLA compared to previous simplified estimates. We also characterize regions of parameter space where emerging and/or semivisible jets could arise from pure-glue dark sectors, thereby providing new benchmark models that motivate searches for these signatures.

Section: Jhep04(2024)070mentioning

confidence: 99%

Dark sector glueballs at the LHC

Batz,

Cohen,

Curtin

et al. 2024

Emerging jet probes of strongly interacting dark sectors

Carrasco,

Zurita

2024

A strongly interacting dark sector can give rise to a class of signatures dubbed dark showers, where in analogy to the strong sector in the Standard Model, the dark sector undergoes its own showering and hadronization, before decaying into Standard Model final states. When the typical decay lengths of the dark sector mesons are larger than a few centimeters (and no larger than a few meters) they give rise to the striking signature of emerging jets, characterized by a large multiplicity of displaced vertices.In this article we consider the general reinterpretation of the CMS search for emerging jets plus prompt jets into arbitrary new physics scenarios giving rise to emerging jets. More concretely, we consider the cases where the SM Higgs mediates between the dark sector and the SM, for several benchmark decay scenarios. Our procedure is validated employing the same model than the CMS emerging jet search. We find that emerging jets can be the leading probe in regions of parameter space, in particular when considering the so-called gluon portal and dark photon portal decay benchmarks. With the current 16.1 fb−1 of luminosity this search can exclude down to $$ \mathcal{O} $$ O (20)% exotic branching ratio of the SM Higgs, but a naive extrapolation to the 139 fb−1 luminosity employed in the current model-independent, indirect bound of 16 % would probe exotic branching ratios into dark quarks down to below 10 %. Further extrapolating these results to the HL-LHC, we find that one can pin down exotic branching ratio values of 1%, which is below the HL-LHC expectations of 2.5–4 %. We make our recasting code publicly available, as part of the LLP Recasting Repository.

Equivariant, safe and sensitive — graph networks for new physics

Bhardwaj,

Englert,

Naskar

et al. 2024

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with IRC-safe principles, our model significantly reduces computational overhead while ensuring theoretical consistency in identifying BSM scenarios amidst Quantum Chromodynamics backgrounds. The proposed GNN architecture demonstrates superior performance in tagging semi-visible jets, highlighting its potential as a robust tool for advancing BSM search strategies at high-energy colliders.