How Invisible: Regressing The Key Model Parameter for Semi-visible Jet Searches

TL;DR

This paper introduces a regression model to accurately reconstruct the key parameter $r_{inv}$ in semi-visible jet events, improving sensitivity and unifying search strategies in collider experiments.

Contribution

A novel regression approach for reconstructing $r_{inv}$ in SVJ events, outperforming previous analytical methods and enabling more effective collider searches.

Findings

The model accurately reconstructs $r_{inv}$ with higher precision.

Performance remains robust across different signal parameters.

The approach can unify $s$-channel and $t$-channel SVJ searches.

Abstract

Semi-visible jets (SVJs) provide a characteristic collider signature of strongly interacting dark sectors, in which the key model parameter $r_{\mathrm{inv}}$ controls the fraction of dark hadrons decaying to dark matter candidates. In this work, a regression model is developed to reconstruct $r_{\mathrm{inv}}$ in SVJ events produced in association with an energetic photon. The model uses information from high-level physics objects only, and the training procedure is optimized to ensure applicability. The performance is found to be robust against varying signal parameters and $r_{\mathrm{inv}}$ can be reconstructed at a much higher precision, compared to previously developed analytical method. It offers a new approach to conduct SVJ searches that can potentially unify both $s$-channel and $t$-channel productions, enhancing the sensitivities.