2021/09/16 by Manuel Drees, Meng Shi, Drees, Manuel +3 · 1 citation
Physics and Astronomy · #Dark Matter and Cosmic Phenomena #Dark matter #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Large Hadron Collider #Lepton #Muon #Neutrino #Neutrino Physics Research #Nuclear physics #Particle physics #Particle physics theoretical and experimental studies #Physics #Physics beyond the Standard Model #Sensitivity (control systems) #Universality (dynamical systems) #hep-ex #hep-ph
paper · pdf · doi:10.48550/arxiv.2109.07674
published in arXiv (Cornell University) (Cornell University) · 39 pages, 10 figures
openalex publication_date 2021/09/16 · openalex created_date 2021/09/27 · arxiv created 2022/02/17 · arxiv updated 2022/02/18 · openalex updated_date 2026/08/06
Extending the Standard Model (SM) by a U(1)Lμ-Lτ group gives potentially significant new contributions to gμ-2, allows the construction of realistic neutrino mass matrices, incorporates lepton universality violation, and offers an anomaly-free mediator for a Dark Matter (DM) sector. In a recent analysis we showed that published LHC searches are not very sensitive to this model. Here we apply several Machine Learning (ML) algorithms in order to distinguish this model from the SM using simulated LHC data. In particular, we optimize the 3μ-signal, which has a considerably larger cross section than the 4μ-signal. Furthermore, since the 2-muon plus missing ET final state gets contributions from diagrams involving DM particles, we optimize it as well. We find greatly improved sensitivity, which already for 36 fb-1 of data exceeds the combination of published LHC and non-LHC results. We also emphasize the usefulness of Boosted Decision Trees which, unlike Neural Networks, easily allow to extract additional information from the data which directly connect to the theoretical model through feature importance. The same scheme could be used to analyze other models.