2024/10/31 by Rafał Masełek, Masełek, Rafał, Mihoko M. Nojiri +3
Computer Science · Physics and Astronomy · #Bar (unit) #Computational Physics and Python Applications #Computer science #Energy (signal processing) #Geometry #Hadron #Large Hadron Collider #Missing energy #Nuclear physics #Particle Detector Development and Performance #Particle physics #Particle physics theoretical and experimental studies #Physics #Plane (geometry) #Production (economics) #Quantum chromodynamics #SIGNAL (programming language) #Sensitivity (control systems) #Transverse plane
paper · pdf · doi:10.48550/arxiv.2411.00093
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD radiation or squark decays. The analysis generally suffers from the large irreducible Z + jets (Z → ν ν) background. In this study, we explore Machine Learning (ML) methods for efficient signal/background discrimination. Our best attempt uses both reconstructed (jets, missing transverse energy, etc.) and low-level (particle-flow) objects. We find that the discrimination performance improves as the pT threshold for soft particles is lowered from 10 GeV to 1 GeV, at the expense of larger systematic uncertainty. In many cases, the ML method provides a factor two enhancement in S/√((S + B)) from a simple kinematical selection. The sensitivity on the squark-elecroweakino mass plane is derived with this method, assuming the Run-3 and HL-LHC luminosities. Moreover, we investigate the relations between input features and the network's classification performance to reveal the physical information used in the background/signal discrimination process.