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We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets.
H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds , Phys. Rev. D 101
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L. Bradshaw, R.K. Mishra, A. Mitridate and B. Ostdiek, Mass Agnostic Jet Taggers , SciPost Phys. 8
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T.G. Dietterich, Ensemble methods in machine learning , in Multiple Classifier Systems , Springer Berlin Heidelberg, (2000), pp. 1–15
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B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
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G. Kasieczka and D. Shih, Robust Jet Classifiers through Distance Correlation , Phys. Rev. Lett. 125
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A. Niculescu-Mizil and R. Caruana, Predicting good probabilities with supervised learning , in Proceedings of the 22nd International Conference on Machine Learning , ICML ’05 , Association for Computing Machinery, New York, NY, U.S.A., (2005), pp. 625-632, [ DOI ]
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C.E. Rasmussen and C.K.I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , The MIT Press (2005)
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C.K. Khosa and V. Sanz, Anomaly Awareness , arXiv:2007.14462 [ IN SPIRE
2007
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2008
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M. Cacciari, G.P. Salam and G. Soyez, The anti- k t k_{t} jet clustering algorithm , JHEP 04
K. Lee, K. Lee, H. Lee and J. Shin, A simple unified framework for detecting out-of-distribution samples and adversarial attacks , in NeurIPS , (2018)
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T. Heimel, G. Kasieczka, T. Plehn and J.M. Thompson, QCD or What? , SciPost Phys. 6
2019
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2019
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M. Hein, M. Andriushchenko and J. Bitterwolf, Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem , in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019) 41
2019
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2008
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J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer and T. Stelzer, MadGraph 5: Going Beyond , JHEP 06
2011
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2012
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C.L. Lan and L. Dinh, Perfect density models cannot guarantee anomaly detection , arXiv:2012.03808
2012
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2012
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M.P. Naeini, G.F. Cooper and M. Hauskrecht, Obtaining well calibrated probabilities using bayesian binning , Proceedings of the…AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence 2015
2015
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2016
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J. Ren et al., Likelihood ratios for out-of-distribution detection , in NeurIPS , (2019)
2019
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L.N. Smith and N. Topin, Super-convergence: very fast training of neural networks using large learning rates , in Defense + Commercial Sensing , (2019)
2019
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M. Farina, Y. Nakai and D. Shih, Searching for New Physics with Deep Autoencoders , Phys. Rev. D 101
2020
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J. Hajer, Y.-Y. Li, T. Liu and H. Wang, Novelty Detection Meets Collider Physics , Phys. Rev. D 101
2020
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2021
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2021
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2021
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T. Cheng, Test sets for jet anomaly detection at the lhc , (2021), [ DOI ]
2021
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B. Ostdiek, Deep Set Auto Encoders for Anomaly Detection in Particle Physics , SciPost Phys. 12
2022
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