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There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider.
Extending the search for new resonances with machine learning ,
J. H. Collins, K. Howe and B. Nachman, · 1902
Earlier work this paper cites.
ParticleNet: Jet Tagging via Particle Clouds ,
H. Qu and L. Gouskos, · 1902
Earlier work this paper cites.
Metric Space of Collider Events ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 1902
Earlier work this paper cites.
A robust anomaly finder based on autoencoders (2019),
T. S. Roy and A. H. Vijay, · 1903
Earlier work this paper cites.
Uncovering latent jet substructure ,
B. M. Dillon, D. A. Faroughy and J. F. Kamenik, · 1904
Earlier work this paper cites.
Adversarially-trained autoencoders for robust unsupervised new physics searches ,
A. Blance, M. Spannowsky and P. Waite, · 1905
Earlier work this paper cites.
Deep Set Prediction Networks ,
Y. Zhang, J. Hare and A. Prügel-Bennett, · 1906
Earlier work this paper cites.
FSPool: Learning Set Representations with Featurewise Sort Pooling ,
Y. Zhang, J. Hare and A. Prügel-Bennett, · 1906
Earlier work this paper cites.
Does SUSY have friends? A new approach for LHC event analysis ,
A. Mullin, S. Nicholls, H. Pacey, M. Parker, M. White and S. Williams, · 1912
Earlier work this paper cites.
Learning multivariate new physics ,
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, · 1912
Earlier work this paper cites.
Transferability of Deep Learning Models in Searches for New Physics at Colliders ,
M. Romão Crispim, N. F. Castro, R. Pedro and T. Vale, · 1912
Earlier work this paper cites.
Anomaly Detection with Density Estimation ,
B. Nachman and D. Shih, · 2001
Earlier work this paper cites.
Simulation Assisted Likelihood-free Anomaly Detection ,
A. Andreassen, B. Nachman and D. Shih, · 2001
Earlier work this paper cites.
Tag N’ Train: a technique to train improved classifiers on unlabeled data ,
O. Amram and C. M. Suarez, · 2002
Earlier work this paper cites.
Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders ,
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro and T. Vale, · 2004
Earlier work this paper cites.
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. , · 2005
Earlier work this paper cites.
Learning the latent structure of collider events ,
B. M. Dillon, D. A. Faroughy, J. F. Kamenik and M. Szewc, · 2005
Earlier work this paper cites.
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark ,
O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, · 2005
Earlier work this paper cites.
Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders ,
M. Crispim Romão, N. F. Castro and R. Pedro, · 2006
Earlier work this paper cites.
Variational Autoencoders for Anomalous Jet Tagging (2020),
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, · 2007
Earlier work this paper cites.
Anomaly Awareness (2020),
C. K. Khosa and V. Sanz, · 2007
Earlier work this paper cites.
Unsupervised Outlier Detection in Heavy-Ion Collisions ,
P. Thaprasop, K. Zhou, J. Steinheimer and C. Herold, · 2007
Earlier work this paper cites.
Mass Unspecific Supervised Tagging (MUST) for boosted jets ,
J. A. Aguilar-Saavedra, F. R. Joaquim and J. F. Seabra, · 2008
Earlier work this paper cites.
Simulation-assisted decorrelation for resonant anomaly detection ,
K. Benkendorfer, L. L. Pottier and B. Nachman, · 2009
Cited alongside, same era.
Anomaly Detection for Physics Analysis and Less than Supervised Learning (2020),
B. Nachman, · 2010
Cited alongside, same era.
Unsupervised clustering for collider physics ,
V. Mikuni and F. Canelli, · 2010
Cited alongside, same era.
Anomaly Detection With Conditional Variational Autoencoders ,
A. A. Pol, V. Berger, G. Cerminara, C. Germain and M. Pierini, · 2010
Cited alongside, same era.
Combining outlier analysis algorithms to identify new physics at the LHC (2020),
M. van Beekveld, S. Caron, L. Hendriks, P. Jackson, A. Leinweber, S. Otten, R. Patrick, R. Ruiz De Austri, M. Santoni and M. White, · 2010
Cited alongside, same era.
Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge ,
Searching for New Physics with Deep Autoencoders ,
M. Farina, Y. Nakai and D. Shih, · 2020
Later among the works it cites.
Lhc simulation project ,
D. community, · 2020
Later among the works it cites.
Unsupervised-hackathon ,
D. community, · 2020
Later among the works it cites.
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics (2021),
G. Kasieczka et al. , · 2021
Closest in time.
Topological Obstructions to Autoencoding ,
J. Batson, C. G. Haaf, Y. Kahn and D. A. Roberts, · 2021
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Unsupervised Event Classification with Graphs on Classical and Photonic Quantum Computers (2021),
A. Blance and M. Spannowsky, · 2021
Closest in time.
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S. E. Park, D. Rankin, S.-M. Udrescu, M. Yunus and P. Harris, · 2011
Cited alongside, same era.
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation ,
G. Stein, U. Seljak and B. Dai, · 2012
Cited alongside, same era.
Uncovering hidden new physics patterns in collider events using Bayesian probabilistic models ,
D. A. Faroughy, · 2012
Cited alongside, same era.
Auto-Encoding Variational Bayes ,
D. P. Kingma and M. Welling, · 2013
Cited alongside, same era.
Adam: A Method for Stochastic Optimization ,
D. P. Kingma and J. Ba, · 2014
Cited alongside, same era.
Variational Inference with Normalizing Flows ,
D. Jimenez Rezende and S. Mohamed, · 2015
Cited alongside, same era.
A generic anti-QCD jet tagger ,
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, · 2017
Cited alongside, same era.
B. Bortolato, B. M. Dillon, J. F. Kamenik and A. Smolkovič, · 2021
Closest in time.
Comparing weak- and unsupervised methods for resonant anomaly detection ,
J. H. Collins, P. Martín-Ramiro, B. Nachman and D. Shih, · 2021
Closest in time.
RanBox: Anomaly Detection in the Copula Space (2021),
T. Dorigo, M. Fumanelli, C. Maccani, M. Mojsovska, G. C. Strong and B. Scarpa, · 2021
Closest in time.
The Data-Directed Paradigm for BSM searches (2021),
S. Volkovich, F. De Vito Halevy and S. Bressler, · 2021
Closest in time.
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
Closest in time.
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests (2021),
P. Chakravarti, M. Kuusela, J. Lei and L. Wasserman, · 2021
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Autoencoders for unsupervised anomaly detection in high energy physics ,
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, · 2021
Closest in time.
Anomaly detection with Convolutional Graph Neural Networks (2021),
O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam and M. Spannowsky, · 2021
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Better Latent Spaces for Better Autoencoders (2021),
B. M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, · 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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The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider (2021),
T. Aarrestad et al. , · 2021
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Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC (2021),
S. Caron, L. Hendriks and R. Verheyen, · 2021
Closest in time.
LHC physics dataset for unsupervised New Physics detection at 40 MHz (2021),
E. Govorkova, E. Puljak, T. Aarrestad, M. Pierini, K. A. Woźniak and J. Ngadiuba, · 2021
Closest in time.
High-dimensional Anomaly Detection with Radiative Return in e + e − e^{+}e^{-} Collisions (2021),
J. Gonski, J. Lai, B. Nachman and I. Ochoa, · 2021
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Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider (2021),
E. Govorkova et al. , · 2021
Closest in time.
Symmetries, Safety, and Self-Supervision (2021),
B. M. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson and L. Vogel, · 2021
Closest in time.
bostdiek/DarkMachines-UnsupervisedChallenge: arXiv_v1 ,
B. Ostdiek, · 2021
Closest in time.