A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty von Nachman, Benjamin

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Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning von Larkoski, Andrew J., Moult, Ian, Nachman, Benjamin

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Classification without labels: learning from mixed samples in high energy physics von Metodiev, Eric M., Nachman, Benjamin, Thaler, Jesse

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Bisection-based asymmetric M T2 computation: a higher precision calculator than existing symmetric methods von Lester, Christopher G., Nachman, Benjamin

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Simulating Collider Physics on Quantum Computers Using Effective Field Theories von Bauer, Christian W., Nachman, Benjamin, Freytsis, Marat

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Elsa: enhanced latent spaces for improved collider simulations von Nachman, Benjamin, Winterhalder, Ramon

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A cautionary tale of decorrelating theory uncertainties von Ghosh, Aishik, Nachman, Benjamin

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Investigating the topology dependence of quark and gluon jets von Bright-Thonney, Samuel, Nachman, Benjamin

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Designing observables for measurements with deep learning von Long, Owen, Nachman, Benjamin

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Simulation-based anomaly detection for multileptons at the LHC von Krzyzanska, Katarzyna, Nachman, Benjamin

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Weakly supervised classification in high energy physics von Dery, Lucio Mwinmaarong, Nachman, Benjamin, Rubbo, Francesco, Schwartzman, Ariel

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Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis von de Oliveira, Luke, Paganini, Michela, Nachman, Benjamin

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Convolved substructure: analytically decorrelating jet substructure observables von Moult, Ian, Nachman, Benjamin, Neill, Duff

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OmniFold: A Method to Simultaneously Unfold All Observables von Andreassen, Anders, Komiske, Patrick T, Metodiev, Eric M, Nachman, Benjamin, Thaler, Jesse

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

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Quantum Algorithm for High Energy Physics Simulations von Nachman, Benjamin, Provasoli, Davide, de Jong, Wibe A, Bauer, Christian W

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Learning likelihood ratios with neural network classifiers von Rizvi, Shahzar, Pettee, Mariel, Nachman, Benjamin

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Comparing weak- and unsupervised methods for resonant anomaly detection von Collins, Jack H., Martín-Ramiro, Pablo, Nachman, Benjamin, Shih, David

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Machine learning templates for QCD factorization in the search for physics beyond the standard model von Lin, Joshua, Bhimji, Wahid, Nachman, Benjamin

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

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