Search for long-lived particles using out-of-time trackless jets in proton-proton collisions at $\sqrt{s}$ = 13 TeV

TL;DR

This paper presents a novel deep learning-based search for long-lived particles decaying outside the inner detector regions in proton-proton collisions at 13 TeV, setting new exclusion limits up to 1.8 TeV.

Contribution

It introduces a new technique combining trackless and out-of-time jet data with deep neural networks to identify long-lived particle decays in CMS data.

Findings

Excludes neutralino masses up to 1.18 TeV for decay lengths around 0.5 m.

Achieves the most sensitive search in the Higgs mass range to date.

Sets new limits on long-lived particle production at the LHC.

Abstract

A search for long-lived particles decaying in the outer regions of the CMS silicon tracker or in the calorimeters is presented. The search is based on a data sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the LHC in 2016-2018, corresponding to an integrated luminosity of 138 fb$^{-1}$. A novel technique, using trackless and out-of-time jet information combined in a deep neural network discriminator, is employed to identify decays of long-lived particles. The results are interpreted in a simplified model of chargino-neutralino production, where the neutralino is the next-to-lightest supersymmetric particle, is long-lived, and decays to a gravitino and either a Higgs or Z boson. This search is most sensitive to neutralino proper decay lengths of approximately 0.5 m, for which masses up to 1.18 TeV are excluded at 95% confidence level. The current search is the best result to date in the mass range from the kinematic limit imposed by the Higgs mass up to 1.8 TeV.