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Searches for anomalies are a significant motivation for the LHC and help define key analysis steps, including triggers.
Dirichlet variational autoencoder ,
W. Joo, W. Lee, S. Park and I.-C. Moon, · 1901
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.
Extending the search for new resonances with machine learning ,
J. H. Collins, K. Howe and B. Nachman, · 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.
Strongly interacting dark sectors in the early Universe and at the LHC through a simplified portal ,
E. Bernreuther, F. Kahlhoefer, M. Krämer and P. Tunney, · 1907
Earlier work this paper cites.
Guided image generation with conditional invertible neural networks (2019),
L. Ardizzone, C. Lüth, J. Kruse, C. Rother and U. Köthe, · 1907
Earlier work this paper cites.
Exploring the Space of Jets with CMS Open Data ,
P. T. Komiske, R. Mastandrea, E. M. Metodiev, P. Naik and J. Thaler, · 1908
Earlier work this paper cites.
Transferability of Deep Learning Models in Searches for New Physics at Colliders ,
M. Romão Crispim, N. Castro, R. Pedro and T. Vale, · 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.
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.
Least square quantization in pcm ,
S. P. Lloyd, · 1982
Earlier work this paper cites.
Simulation Assisted Likelihood-free Anomaly Detection ,
A. Andreassen, B. Nachman and D. Shih, · 2001
Earlier work this paper cites.
Anomaly Detection with Density Estimation ,
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.
A quantum algorithm for model independent searches for new physics (2020),
K. T. Matchev, P. Shyamsundar and J. Smolinsky, · 2003
Earlier work this paper cites.
The Hidden Geometry of Particle Collisions ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 2004
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.
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.
G. Aad et al. , · 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.
M. Crispim Romão, N. F. Castro and R. Pedro, · 2006
Earlier work this paper cites.
Casting a graph net to catch dark showers ,
E. Bernreuther, T. Finke, F. Kahlhoefer, M. Krämer and A. Mück, · 2006
Earlier work this paper cites.
Pattern Recognition and Machine Learning ,
C. M. Bishop, · 2006
Earlier work this paper cites.
Invertible Networks or Partons to Detector and Back Again ,
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, A. Rousselot, R. Winterhalder, L. Ardizzone and U. Köthe, · 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.
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.
Echoes of a hidden valley at hadron colliders ,
M. J. Strassler and K. M. Zurek, · 2007
Earlier work this paper cites.
K-means++: The advantages of careful seeding ,
D. Arthur and S. Vassilvitskii, · 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.
The anti- k t k_{t} jet clustering algorithm ,
M. Cacciari, G. P. Salam and G. Soyez, · 2008
Earlier work this paper cites.
Introduction to information retrieval ,
C. D. Manning, P. Raghavan and H. Schütze, · 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.
Unsupervised clustering for collider physics ,
V. Mikuni and F. Canelli, · 2010
Cited alongside, same era.
Combining outlier analysis algorithms to identify new physics at the LHC ,
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.
Visible Effects of Invisible Hidden Valley Radiation ,
L. Carloni and T. Sjostrand, · 2010
Cited alongside, same era.
Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge ,
Glow: Generative flow with invertible 1x1 convolutions ,
D. P. Kingma and P. Dhariwal, · 2018
Later among the works it cites.
Analyzing inverse problems with invertible neural networks (2018),
L. Ardizzone, J. Kruse, S. Wirkert, D. Rahner, E. W. Pellegrini, R. S. Klessen, L. Maier-Hein, C. Rother and U. Köthe, · 2018
Later among the works it cites.
Pulling Out All the Tops with Computer Vision and Deep Learning ,
S. Macaluso and D. Shih, · 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 ,
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S. E. Park, D. Rankin, S.-M. Udrescu, M. Yunus and P. Harris, · 2011
Cited alongside, same era.
Discerning Secluded Sector gauge structures ,
L. Carloni, J. Rathsman and T. Sjostrand, · 2011
Cited alongside, same era.
Multivariate discrimination and the Higgs + W/Z search ,
J. Gallicchio, J. Huth, M. Kagan, M. D. Schwartz, K. Black and B. Tweedie, · 2011
Cited alongside, same era.
Identifying Boosted Objects with N-subjettiness ,
J. Thaler and K. Van Tilburg, · 2011
Cited alongside, same era.
Scikit-learn: Machine Learning in Python ,
F. Pedregosa et al. , · 2011
Cited alongside, same era.
D. E. Morrissey, T. Plehn and T. M. P. Tait, · 2012
Cited alongside, same era.
M. Cacciari, G. P. Salam and G. Soyez, · 2012
Cited alongside, same era.
How to GAN Higher Jet Resolution (2020),
P. Baldi, L. Blecher, A. Butter, J. Collado, J. N. Howard, F. Keilbach, T. Plehn, G. Kasieczka and D. Whiteson, · 2012
Cited alongside, same era.
O. Cerri, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, · 2019
Later among the works it cites.
Learning New Physics from a Machine ,
R. T. D’Agnolo and A. Wulzer, · 2019
Later among the works it cites.
Guiding New Physics Searches with Unsupervised Learning ,
A. De Simone and T. Jacques, · 2019
Later among the works it cites.
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. , · 2019
Later among the works it cites.
Searching for New Physics with Deep Autoencoders ,
M. Farina, Y. Nakai and D. Shih, · 2020
Later among the works it cites.
Novelty Detection Meets Collider Physics ,
J. Hajer, Y.-Y. Li, T. Liu and H. Wang, · 2020
Later among the works it cites.
Autoencoders for unsupervised anomaly detection in high energy physics ,
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, · 2021
Later among the works it cites.
Better Latent Spaces for Better Autoencoders ,
B. M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, · 2021
Later among the works it cites.
Anomaly detection with Convolutional Graph Neural Networks ,
O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam and M. Spannowsky, · 2021
Later among the works it cites.
Anomalous jet identification via sequence modeling ,
A. Kahn, J. Gonski, I. Ochoa, D. Williams and G. Brooijmans, · 2021
Later among the works it cites.
Meta-learning and data augmentation for mass-generalised jet taggers (2021),
M. J. Dolan and A. Ore, · 2021
Later among the works it cites.
Autoencoders for Semivisible Jet Detection (2021),
F. Canelli, A. de Cosa, L. L. Pottier, J. Niedziela, K. Pedro and M. Pierini, · 2021
Later among the works it cites.
P. Jawahar, T. Aarrestad, N. Chernyavskaya, M. Pierini, K. A. Wozniak, J. Ngadiuba, J. Duarte and S. Tsan, · 2021
Later among the works it cites.
Online-compatible Unsupervised Non-resonant Anomaly Detection (2021),
V. Mikuni, B. Nachman and D. Shih, · 2021
Later among the works it cites.
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
Later among the works it cites.
Anomaly detection in high-energy physics using a quantum autoencoder (2021),
V. S. Ngairangbam, M. Spannowsky and M. Takeuchi, · 2021
Later among the works it cites.
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics ,
G. Kasieczka et al. , · 2021
Later among the works it cites.
Bump Hunting in Latent Space (2021),
B. Bortolato, B. M. Dillon, J. F. Kamenik and A. Smolkovič, · 2021
Later among the works it cites.
Topological Obstructions to Autoencoding ,
J. Batson, C. G. Haaf, Y. Kahn and D. A. Roberts, · 2021
Later among the works it cites.
RanBox: Anomaly Detection in the Copula Space (2021),
T. Dorigo, M. Fumanelli, C. Maccani, M. Mojsovska, G. C. Strong and B. Scarpa, · 2021
Later among the works it cites.
S. Caron, L. Hendriks and R. Verheyen, · 2021
Later among the works it cites.
Challenges for Unsupervised Anomaly Detection in Particle Physics (2021),
K. Fraser, S. Homiller, R. K. Mishra, B. Ostdiek and M. D. Schwartz, · 2021
Later among the works it cites.
Perturbative benchmark models for a dark shower search program ,
S. Knapen, J. Shelton and D. Xu, · 2021
Later among the works it cites.
Unsupervised hadronic SUEP at the LHC ,
J. Barron, D. Curtin, G. Kasieczka, T. Plehn and A. Spourdalakis, · 2021
Later among the works it cites.
Symmetries, Safety, and Self-Supervision (2021),
B. M. Dillon, G. Kasieczka, H. Olischlager, T. Plehn, P. Sorrenson and L. Vogel, · 2021
Later among the works it cites.
Understanding Event-Generation Networks via Uncertainties (2021),
M. Bellagente, M. Haußmann, M. Luchmann and T. Plehn, · 2021
Later among the works it cites.
Generative Networks for Precision Enthusiasts (2021),
A. Butter, T. Heimel, S. Hummerich, T. Krebs, T. Plehn, A. Rousselot and S. Vent, · 2021
Later among the works it cites.
Inference of cosmic-ray source properties by conditional invertible neural networks (2021),
T. Bister, M. Erdmann, U. Köthe and J. Schulte, · 2021
Later among the works it cites.
T. Aarrestad, M. van Beekveld, M. Bona, A. Boveia, S. Caron, J. Davies, A. D. Simone, C. Doglioni, J. M. Duarte, A. Farbin, H. Gupta, L. Hendriks et al. , · 2022
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