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We propose a new method to define anomaly scores and apply this to particle physics collider events.
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer ,
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Dijet resonance search with weak supervision using s = 13 \sqrt{s}=13 TeV p p pp collisions in the ATLAS detector ,
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Combining outlier analysis algorithms to identify new physics at the LHC (2020),
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Combining outlier analysis algorithms to identify new physics at the lhc (2020), 2010.07940
M. van Beekveld, S. Caron, L. Hendriks, P. Jackson, A. Leinweber, S. Otten, R. Patrick, R. R. de Austri, M. Santoni and M. White, · 2010
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Phase Space Sampling and Inference from Weighted Events with Autoregressive Flows ,
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MADE: Masked Autoencoder for Distribution Estimation ,
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Anomaly Detection for Resonant New Physics with Machine Learning ,
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Variational Autoencoders for New Physics Mining at the Large Hadron Collider ,
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Weakly Supervised Classification in High Energy Physics ,
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Masked Autoregressive Flow for Density Estimation ,
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Evidence for t t ¯ t t ¯ t\bar{t}t\bar{t} production in the multilepton final state in proton–proton collisions at s \sqrt{s} =13 tev with the atlas detector ,
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Topological Obstructions to Autoencoding ,
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Event generation and statistical sampling for physics with deep generative models and a density information buffer ,
S. Otten, S. Caron, W. de Swart, M. van Beekveld, L. Hendriks, C. van Leeuwen, D. Podareanu, R. Ruiz de Austri and R. Verheyen, · 2021
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