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Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities.
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The motivation and status of two-body resonance decays after the LHC Run 2 and beyond ,
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Tag N’ Train: A Technique to Train Improved Classifiers on Unlabeled Data (2020),
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Echoes of a hidden valley at hadron colliders ,
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The anti- 𝐤 𝐭 k_{t} jet clustering algorithm ,
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DCTRGAN: Improving the Precision of Generative Models with Reweighting ,
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Visible Effects of Invisible Hidden Valley Radiation ,
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A connection between score matching and denoising autoencoders ,
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Discerning Secluded Sector gauge structures ,
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DELPHES 3: A modular framework for fast-simulation of generic collider experiments ,
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Generative Adversarial Networks (2014),
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Variational inference with normalizing flows ,
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An introduction to PYTHIA 8.2 ,
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Deep residual learning for image recognition (2015), 1512.03385
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Classification without labels: Learning from mixed samples in high energy physics ,
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser and I. Polosukhin, · 2017
Cited alongside, same era.
Anomaly Detection for Resonant New Physics with Machine Learning ,
J. H. Collins, K. Howe and B. Nachman, · 2018
Cited alongside, same era.
Novelty Detection Meets Collider Physics (2018),
J. Hajer, Y.-Y. Li, T. Liu and H. Wang, · 2018
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Searching for New Physics with Deep Autoencoders (2018),
M. Farina, Y. Nakai and D. Shih, · 2018
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JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics (2018),
A. Andreassen, I. Feige, C. Frye and M. D. Schwartz, · 2018
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The unexplored landscape of two-body resonances ,
Classifying Anomalies THrough Outer Density Estimation (CATHODE) (2021),
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2021
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Latent Space Refinement for Deep Generative Models (2021),
R. Winterhalder, M. Bellagente and B. Nachman, · 2021
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Tech. rep., CERN, Geneva (2022)
Anomaly detection search for new resonances decaying into a Higgs boson and a generic new particle 𝐗 X in hadronic final states using 𝐬 \sqrt{s} = 13 TeV 𝐩 𝐩 pp collisions with the ATLAS detector , · 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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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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CaloFlow for CaloChallenge Dataset 1 (2022),
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N. Craig, P. Draper, K. Kong, Y. Ng and D. Whiteson, · 2019
Cited alongside, same era.
Learning New Physics from a Machine ,
R. T. D’Agnolo and A. Wulzer, · 2019
Cited alongside, same era.
QCD or What? ,
T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, · 2019
Cited alongside, same era.
Top quark tagging reference dataset ,
G. Kasieczka, T. Plehn, J. Thompson and M. Russel, · 2019
Cited alongside, same era.
Dijet resonance search with weak supervision using 𝐬 = 𝟏𝟑 \sqrt{s}=13 TeV 𝐩 𝐩 pp collisions in the ATLAS detector ,
G. Aad et al. , · 2020
Cited alongside, same era.
Normalizing Flows: An Introduction and Review of Current Methods ,
I. Kobyzev, S. Prince and M. Brubaker, · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models ,
J. Ho, A. Jain and P. Abbeel, · 2020
Cited alongside, same era.
C. Krause, I. Pang and D. Shih, · 2022
Later among the works it cites.
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows (2022),
B. Käch, D. Krücker, I. Melzer-Pellmann, M. Scham, S. Schnake and A. Verney-Provatas, · 2022
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Event Generation and Density Estimation with Surjective Normalizing Flows ,
R. Verheyen, · 2022
Later among the works it cites.
Score-based generative models for calorimeter shower simulation ,
V. Mikuni and B. Nachman, · 2022
Later among the works it cites.
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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Maximum likelihood training for score-based diffusion odes by high order denoising score matching ,
C. Lu, K. Zheng, F. Bao, J. Chen, C. Li and J. Zhu, · 2022
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Via Machinae 2.0: Full-Sky, Model-Agnostic Search for Stellar Streams in Gaia DR2 (2023),
D. Shih, M. R. Buckley and L. Necib, · 2023
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Weakly-Supervised Anomaly Detection in the Milky Way (2023),
M. Pettee, S. Thanvantri, B. Nachman, D. Shih, M. R. Buckley and J. H. Collins, · 2023
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Search for new phenomena in two-body invariant mass distributions using unsupervised machine learning for anomaly detection at 𝐬 = 𝟏𝟑 \sqrt{s}=13 TeV with the ATLAS detector (2023)
2023
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Anomaly detection under coordinate transformations ,
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee and D. Shih, · 2023
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Anomalies, Representations, and Self-Supervision (2023),
B. M. Dillon, L. Favaro, F. Feiden, T. Modak and T. Plehn, · 2023
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Learning the language of QCD jets with transformers (2023),
T. Finke, M. Krämer, A. Mück and J. Tönshoff, · 2023
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L2LFlows: Generating High-Fidelity 3D Calorimeter Images (2023),
S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, C. Krause, I. Shekhzadeh and D. Shih, · 2023
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Inductive CaloFlow (2023),
M. R. Buckley, C. Krause, I. Pang and D. Shih, · 2023
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PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics (2023),
M. Leigh, D. Sengupta, G. Quétant, J. A. Raine, K. Zoch and T. Golling, · 2023
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Fast Point Cloud Generation with Diffusion Models in High Energy Physics (2023),
V. Mikuni, B. Nachman and M. Pettee, · 2023
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CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation (2023),
E. Buhmann, S. Diefenbacher, E. Eren, F. Gaede, G. Kasieczka, A. Korol, W. Korcari, K. Krüger and P. McKeown, · 2023
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Jet Diffusion versus JetGPT – Modern Networks for the LHC (2023),
A. Butter, N. Huetsch, S. P. Schweitzer, T. Plehn, P. Sorrenson and J. Spinner, · 2023
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ELSA – Enhanced latent spaces for improved collider simulations (2023),
B. Nachman and R. Winterhalder, · 2023
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How to Understand Limitations of Generative Networks (2023),
R. Das, L. Favaro, T. Heimel, C. Krause, T. Plehn and D. Shih, · 2023
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