ATLAS uses AI to hunt for new physics, finds nothing—yet
Physicists deployed machine learning to sift through the largest particle collision dataset ever analyzed, looking for signs of undiscovered particles. They found no evidence of new physics in multilepton events—but the methodology opens a new frontier for discovering rare phenomena that traditional searches might miss.
Originaltitel: Search for Beyond the Standard Model physics with anomaly detection in multilepton final states in pp collisions at $$\sqrt{s}=13~\textrm{TeV}$$ with the ATLAS detector
Abstract A model-agnostic search for Beyond the Standard Model physics is presented, targeting final states with at least four light leptons (electrons or muons). The search regions are separated by event topology and unsupervised machine learning is used to identify anomalous events in the full 140 fb $$^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow/> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:mmultiscripts> </mml:math> of proton–proton collision data collected with the ATLAS detector during Run 2. No significant excess above the Standard Model background expectation is observed. Model-agnostic limits are presented in each topology, along with limits on several benchmark models including vector-like leptons, wino-like charginos and neutralinos, or smuons. Limits are set on the flavourful vector-like lepton model for the first time.