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Semi-visible jets, energy-based models, and self-supervision
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We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approximately invariant under relevant transformations. We use background-enhanced data to create a sensitive representation and evaluate the representations using a CLR-inspired anomaly score and a normalized autoencoder as density estimators. Our results show a remarkable sensitivity for a wide range of semi-visible jets and are more robust than a supervised classifier trained on a specific signal.
Forward citations
Cited by 3 Pith papers
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Neural Control Variates at LO and NLO
Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.
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Agentic Re-Casting using Agentic Re-Simulations
An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.
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Search for new physics in final states with semi-visible jets or anomalous signatures using the ATLAS detector
ATLAS finds no sign of semi-visible jets from Z' decays and excludes Z' masses from 2000 to 3200 GeV for invisible fractions between 0.2 and 0.37.
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