2025/02/14 by Louie Corpe, Corpe, Louie, Abdelhamid Haddad +3 · 1 citation
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies
paper · pdf · doi:10.48550/arxiv.2502.10231
openalex publication_date 2025/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
This note describes the validation of a new form of re-interpretation material provided by an ATLAS search for hadronically-decaying neutral long-lived particles in association with jets or leptons, using the full Run-2 dataset. This reference ATLAS analysis provided a set of machine-learning-based "surrogate models" which return the probability of an event being selected in a given channel of the analysis, using as input truth-level kinematic information (decay position, transverse momentum and decay products of the long-lived particles). In this document, we describe the surrogate model framework in detail, and how it responds to issues identified in other re-interpretation procedures. We describe independent validations of the surrogate models' performance in reproducing the original analysis results -- first using a standalone framework and then employing the HackAnalysis framework.