Quantum anomaly detection for collider physics by Sulaiman Alvi, Christian W. Bauer, Benjamin Nachman

“…Abstract We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. …”
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Simulation-based anomaly detection for multileptons at the LHC by Katarzyna Krzyzanska, Benjamin Nachman

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Non-resonant anomaly detection with background extrapolation by Kehang Bai, Radha Mastandrea, Benjamin Nachman

“…Abstract Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. …”
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Resonant anomaly detection with multiple reference datasets by Mayee F. Chen, Benjamin Nachman, Frederic Sala

“…Abstract An important class of techniques for resonant anomaly detection in high energy physics builds models that can distinguish between reference and target datasets, where only the latter has appreciable signal. …”
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The interplay of machine learning-based resonant anomaly detection methods by Tobias Golling, Gregor Kasieczka, Claudius Krause, Radha Mastandrea, Benjamin Nachman, John Andrew Raine, Debajyoti Sengupta, David Shih, Manuel Sommerhalder

“…Abstract Machine learning-based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). …”
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High-dimensional anomaly detection with radiative return in e+e− collisions by Julia Gonski, Jerry Lai, Benjamin Nachman, Inês Ochoa

“…We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.…”
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