Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiveness of these approaches in enhancing sensitivity to various simulated signal samples is studied and compared using data collected in proton–proton collisions at a center-of-mass energy of 13 (Formula presented) (Formula presented). In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.
Machine-learning techniques for model-independent searches in dijet final states
Abbrescia M.;Barbieri M.;Buonsante M.;Colaleo A.;Elmetenawee W.;Generoso L.;Louka M.;Mastrapasqua V.;My S.;Nenna F.;Nuzzo S.;Pellecchia A.;Pompili A.;Radogna R.;Stamerra A.;Troiano D.;Venditti R.;Zaza A.
2026-01-01
Abstract
Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiveness of these approaches in enhancing sensitivity to various simulated signal samples is studied and compared using data collected in proton–proton collisions at a center-of-mass energy of 13 (Formula presented) (Formula presented). In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
