Searching for high speed long-lived charged massive particles at the LHC

TL;DR

This paper proposes a new method using energy deposition and machine learning to improve the search for high-speed, long-lived charged massive particles at the LHC, enhancing detection capabilities beyond traditional slow-particle techniques.

Contribution

It introduces a boosted decision tree approach leveraging energy deposition data to distinguish CHAMPs from muons at high momentum, expanding search sensitivity at the LHC.

Findings

Enhanced CHAMP detection potential at high velocities.

Ability to differentiate di-CHAMP and CHAMP-muon resonances from muon pairs.

Updated mass limits for CHAMP models based on new analysis.

Abstract

The conventional way to search for long-lived CHArged Massive Particles (CHAMPs) is to identify slow (small $\beta$) tracks using delayed time of flight and high ionization energy loss. But at the 7-14 TeV center of mass energy of the LHC, a CHAMP may be highly boosted (high $\beta$) and therefore look more like a minimum ionizing particle, while for high momentum muons (more than $\sim$500 GeV/c) the radiative effect dominates energy deposition. This suggests a new strategy to search for CHAMPs at the LHC. Using energy deposition from different detector components, we construct a boosted decision tree discriminant to separate high momentum CHAMPs from high momentum muons. This method increases substantially the CHAMP search potential and it can be used to distinguish possible di-CHAMP or CHAMP-muon resonance models from di-muon resonance models. We illustrate the new method using a mGMSB model and a recently proposed di-CHAMP model and we give updated CHAMP mass limits for these two models using the recent CDF CHAMP results.