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We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.
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The Machine Learning Landscape of Top Taggers ,
G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath, B. M. Dillon, M. Fairbairn, D. A. Faroughy, W. Fedorko, C. Gay, L. Gouskos, J. F. Kamenik et al. , · 1902
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Simulation Assisted Likelihood-free Anomaly Detection ,
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Tag N’ Train: a technique to train improved classifiers on unlabeled data ,
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Variational Autoencoders for Anomalous Jet Tagging (2020),
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The anti- k t k_{t} jet clustering algorithm ,
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Anomaly Detection With Conditional Variational Autoencoders ,
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Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge (2020),
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M. Cacciari, G. P. Salam and G. Soyez, · 2012
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A general search for new phenomena with the ATLAS detector in pp collisions at s = 8 \sqrt{s}=8 TeV , · 2014
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DELPHES 3, A modular framework for fast simulation of a generic collider experiment ,
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens and M. Selvaggi, · 2014
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An Introduction to PYTHIA 8.2 ,
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MUSiC, a Model Unspecific Search for New Physics, in pp Collisions at s = 8 \sqrt{s}=8 TeV (2017)
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Classification without labels: Learning from mixed samples in high energy physics ,
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Anomaly Detection for Resonant New Physics with Machine Learning ,
J. H. Collins, K. Howe and B. Nachman, · 2018
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T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, · 2019
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Variational Autoencoders for New Physics Mining at the Large Hadron Collider ,
O. Cerri, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, · 2019
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Searching for New Physics with Deep Autoencoders ,
M. Farina, Y. Nakai and D. Shih, · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances ,
J. Tack, S. Mo, J. Jeong and J. Shin, · 2020
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Anomaly detection with convolutional Graph Neural Networks ,
O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam and M. Spannowsky, · 2021
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Particle Graph Autoencoders and Differentiable, Learned Energy Mover’s Distance ,
Autoencoders for semivisible jet detection ,
F. Canelli, A. de Cosa, L. L. Pottier, J. Niedziela, K. Pedro and M. Pierini, · 2022
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Lorentz Group Equivariant Autoencoders (2022),
Z. Hao, R. Kansal, J. Duarte and N. Chernyavskaya, · 2022
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IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection ,
O. Atkinson, A. Bhardwaj, C. Englert, P. Konar, V. S. Ngairangbam and M. Spannowsky, · 2022
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Creating simple, interpretable anomaly detectors for new physics in jet substructure ,
L. Bradshaw, S. Chang and B. Ostdiek, · 2022
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S. Tsan, R. Kansal, A. Aportela, D. Diaz, J. Duarte, S. Krishna, F. Mokhtar, J.-R. Vlimant and M. Pierini, · 2021
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Deep Set Auto Encoders for Anomaly Detection in Particle Physics (2021),
B. Ostdiek, · 2021
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Unsupervised Hadronic SUEP at the LHC ,
J. Barron, D. Curtin, G. Kasieczka, T. Plehn and A. Spourdalakis, · 2021
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Anomalous jet identification via sequence modeling ,
A. Kahn, J. Gonski, I. Ochoa, D. Williams and G. Brooijmans, · 2021
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Bump Hunting in Latent Space (2021),
B. Bortolato, B. M. Dillon, J. F. Kamenik and A. Smolkovič, · 2021
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Better Latent Spaces for Better Autoencoders ,
B. M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, · 2021
Cited alongside, same era.
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics (2021),
G. Kasieczka et al. , · 2021
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Estimating Galactic Distances From Images Using Self-supervised Representation Learning (2021),
M. A. Hayat, P. Harrington, G. Stein, Z. Lukić and M. Mustafa, · 2021
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J. F. Kamenik and M. Szewc, · 2022
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Online-compatible unsupervised nonresonant anomaly detection ,
V. Mikuni, B. Nachman and D. Shih, · 2022
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A Normalized Autoencoder for LHC Triggers (2022),
B. M. Dillon, L. Favaro, T. Plehn, P. Sorrenson and M. Krämer, · 2022
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Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows ,
P. Jawahar, T. Aarrestad, N. Chernyavskaya, M. Pierini, K. A. Wozniak, J. Ngadiuba, J. Duarte and S. Tsan, · 2022
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S. Caron, L. Hendriks and R. Verheyen, · 2022
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What’s Anomalous in LHC Jets? (2022),
T. Buss, B. M. Dillon, T. Finke, M. Krämer, A. Morandini, A. Mück, I. Oleksiyuk and T. Plehn, · 2022
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T. Aarrestad et al. , · 2022
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Anomaly Detection under Coordinate Transformations (2022),
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee and D. Shih, · 2022
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Particle Transformer for Jet Tagging (2022),
H. Qu, C. Li and S. Qian, · 2022
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A. Bogatskiy, T. Hoffman, D. W. Miller and J. T. Offermann, · 2022
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Symmetries, safety, and self-supervision ,
B. M. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson and L. Vogel, · 2022
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Self-supervised anomaly detection for new physics ,
B. M. Dillon, R. Mastandrea and B. Nachman, · 2022
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A method to challenge symmetries in data with self-supervised learning ,
R. Tombs and C. G. Lester, · 2022
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Invariant representation driven neural classifier for anti-QCD jet tagging ,
T. Cheng and A. Courville, · 2022
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Mining for Strong Gravitational Lenses with Self-supervised Learning ,
G. Stein, J. Blaum, P. Harrington, T. Medan and Z. Lukic, · 2022
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Self-supervised component separation for the extragalactic submillimeter sky (2022),
V. Bonjean, H. Tanimura, N. Aghanim, T. Bonnaire and M. Douspis, · 2022
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Self-supervised anomaly detection: A survey and outlook ,
H. Hojjati, T. K. K. Ho and N. Armanfard, · 2022
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LHC physics dataset for unsupervised New Physics detection at 40 MHz ,
E. Govorkova, E. Puljak, T. Aarrestad, M. Pierini, K. A. Woźniak and J. Ngadiuba, · 2022
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Exploring Optimal Transport for Event-Level Anomaly Detection at the Large Hadron Collider (2024),
N. Craig, J. N. Howard and H. Li, · 2024
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