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Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC.
Extending the search for new resonances with machine learning ,
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A robust anomaly finder based on autoencoders (2019),
T. S. Roy and A. H. Vijay, · 1903
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R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, · 1912
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Anomaly Detection with Density Estimation ,
B. Nachman and D. Shih, · 2001
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Simulation Assisted Likelihood-free Anomaly Detection ,
A. Andreassen, B. Nachman and D. Shih, · 2001
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The ATLAS Experiment at the CERN Large Hadron Collider ,
ATLAS Collaboration, · 2008
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The CMS Experiment at the CERN LHC ,
CMS Collaboration, · 2008
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A Brief Introduction to PYTHIA 8.1 ,
T. Sjöstrand, S. Mrenna and P. Z. Skands, · 2008
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The anti- k t k_{t} jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
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Data-driven Estimation of Background Distribution through Neural Autoregressive Flows (2020),
S. Choi, J. Lim and H. Oh, · 2008
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Simulation-assisted decorrelation for resonant anomaly detection ,
K. Benkendorfer, L. L. Pottier and B. Nachman, · 2009
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Identifying boosted objects with n-subjettiness ,
J. Thaler and K. Van Tilburg, · 2011
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ATLAS Collaboration, · 2012
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Observation of a New Boson at a Mass of 125 GeV with the CMS Experiment at the LHC ,
CMS Collaboration, · 2012
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M. Cacciari, G. P. Salam and G. Soyez, · 2012
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Sinkhorn distances: Lightspeed computation of optimal transportation distances (2013), 1306.0895
M. Cuturi, · 2013
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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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A generic anti-QCD jet tagger ,
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, · 2017
Cited alongside, same era.
Classification without labels: Learning from mixed samples in high energy physics ,
E. M. Metodiev, B. Nachman and J. Thaler, · 2017
Cited alongside, same era.
T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Adversarially-trained autoencoders for robust unsupervised new physics searches ,
A. Blance, M. Spannowsky and P. Waite, · 2019
Cited alongside, same era.
CMS SUS physics results summary ,
CMS Collaboration, · 2022
Later among the works it cites.
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
Later among the works it cites.
Learning new physics efficiently with nonparametric methods (2022),
M. Letizia, G. Losapio, M. Rando, G. Grosso, A. Wulzer, M. Pierini, M. Zanetti and L. Rosasco, · 2022
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T. Aarrestad et al. , · 2022
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Resonant Anomaly Detection with Multiple Reference Datasets (2022),
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Learning new physics from a machine ,
R. T. D’Agnolo and A. Wulzer, · 2019
Cited alongside, same era.
Guiding new physics searches with unsupervised learning ,
A. D. Simone and T. Jacques, · 2019
Cited alongside, same era.
R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge ,
G. Kasieczka, B. Nachman and D. Shih, · 2019
Cited alongside, same era.
Novelty Detection Meets Collider Physics ,
J. Hajer, Y.-Y. Li, T. Liu and H. Wang, · 2020
Cited alongside, same era.
Searching for New Physics with Deep Autoencoders ,
M. Farina, Y. Nakai and D. Shih, · 2020
Cited alongside, same era.
SUSY Summary Plots June 2021 ,
ATLAS Collaboration, · 2021
Cited alongside, same era.
Summary Plots from ATLAS Searches for Pair-Produced Leptoquarks ,
ATLAS Collaboration, · 2021
Cited alongside, same era.
M. F. Chen, B. Nachman and F. Sala, · 2022
Later among the works it cites.
Self-supervised anomaly detection for new physics ,
B. M. Dillon, R. Mastandrea and B. Nachman, · 2022
Later among the works it cites.
Boosting mono-jet searches with model-agnostic machine learning ,
T. Finke, M. Krämer, M. Lipp and A. Mück, · 2022
Later among the works it cites.
Classifying anomalies through outer density estimation ,
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih and M. Sommerhalder, · 2022
Later among the works it cites.
Resonant anomaly detection without background sculpting (2022),
A. Hallin, G. Kasieczka, T. Quadfasel, D. Shih and M. Sommerhalder, · 2022
Later among the works it cites.
FETA: Flow-Enhanced Transportation for Anomaly Detection (2022),
T. Golling, S. Klein, R. Mastandrea and B. Nachman, · 2022
Later among the works it cites.
Flows for flows: Training normalizing flows between arbitrary distributions with maximum likelihood estimation ,
S. Klein, J. A. Raine and T. Golling, · 2022
Later among the works it cites.
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning ,
R. Mastandrea and B. Nachman, · 2022
Later among the works it cites.
Anomalies, Representations, and Self-Supervision (2023),
B. M. Dillon, L. Favaro, F. Feiden, T. Modak and T. Plehn, · 2023
Closest in time.
Anomaly detection under coordinate transformations ,
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee and D. Shih, · 2023
Closest in time.
CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals ,
J. A. Raine, S. Klein, D. Sengupta and T. Golling, · 2023
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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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