Fetching the paper…
Reading the bibliography…
Anomaly detection is a promising, model-agnostic strategy to find physics beyond the Standard Model.
Metric Space of Collider Events ,
P. T. Komiske, E. M. Metodiev and J. Thaler, · 1902
Earlier work this paper cites.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection (2019), 1904.02639
D. Gong, L. Liu, V. Le, B. Saha, M. R. Mansour, S. Venkatesh and A. van den Hengel, · 1904
Earlier work this paper cites.
Nearest neighbor pattern classification ,
T. Cover and P. Hart, · 1967
Earlier work this paper cites.
Support vector method for novelty detection ,
B. Schölkopf, R. C. Williamson, A. Smola, J. Shawe-Taylor and J. Platt, · 1999
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.
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.
Linearized optimal transport for collider events ,
T. Cai, J. Cheng, N. Craig and K. Craig, · 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.
Anomaly Detection for Physics Analysis and Less than Supervised Learning (2020),
B. Nachman, · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python ,
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos et al. , · 2011
Earlier work this paper cites.
Gromov–wasserstein distances and the metric approach to object matching ,
F. Mémoli, · 2011
Earlier work this paper cites.
Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc ,
G. Aad, T. Abajyan, B. Abbott, J. Abdallah, S. Abdel Khalek, A. Abdelalim, O. Abdinov, R. Aben, B. Abi, M. Abolins and et al., · 2012
Earlier work this paper cites.
Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc ,
S. Chatrchyan, V. Khachatryan, A. Sirunyan, A. Tumasyan, W. Adam, E. Aguilo, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan and et al., · 2012
Earlier work this paper cites.
FastJet User Manual ,
M. Cacciari, G. P. Salam and G. Soyez, · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
An introduction to PYTHIA 8.2 ,
T. Sjöstrand, S. Ask, J. R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C. O. Rasmussen and P. Z. Skands, · 2015
Earlier work this paper cites.
What is the Machine Learning? ,
S. Chang, T. Cohen and B. Ostdiek, · 2018
Earlier work this paper cites.
QCD or What? ,
T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, · 2019
Earlier work this paper cites.
Searching for New Physics with Deep Autoencoders ,
M. Farina, Y. Nakai and D. Shih, · 2020
Earlier work this paper cites.
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics ,
G. Kasieczka et al. , · 2021
Cited alongside, same era.
Topological Obstructions to Autoencoding ,
J. Batson, C. G. Haaf, Y. Kahn and D. A. Roberts, · 2021
Cited alongside, same era.
Autoencoders for unsupervised anomaly detection in high energy physics ,
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, · 2021
Cited alongside, same era.
Better Latent Spaces for Better Autoencoders ,
B. M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, · 2021
Cited alongside, same era.
Pot: Python optimal transport ,
R. Flamary, N. Courty, A. Gramfort, M. Z. Alaya, A. Boisbunon, S. Chambon, L. Chapel, A. Corenflos, K. Fatras, N. Fournier, L. Gautheron, N. T. Gayraud et al. , · 2021
Cited alongside, same era.
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
Boosting sensitivity to new physics with unsupervised anomaly detection in dijet resonance search (2023),
S. V. Chekanov and R. Zhang, · 2023
Later among the works it cites.
Machine Learning for Anomaly Detection in Particle Physics (2023),
V. Belis, P. Odagiu and T. K. Årrestad, · 2023
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
Later among the works it cites.
Unravelling physics beyond the standard model with classical and quantum anomaly detection (2023),
J. Schuhmacher, L. Boggia, V. Belis, E. Puljak, M. Grossi, M. Pierini, S. Vallecorsa, F. Tacchino, P. Barkoutsos and I. Tavernelli, · 2023
Later among the works it cites.
Anomaly Detection in Presence of Irrelevant Features (2023),
M. Freytsis, M. Perelstein and Y. C. San, · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider ,
T. Aarrestad et al. , · 2022
Cited alongside, same era.
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider ,
E. Govorkova et al. , · 2022
Cited alongside, same era.
A Normalized Autoencoder for LHC Triggers (2022),
B. M. Dillon, L. Favaro, T. Plehn, P. Sorrenson and M. Krämer, · 2022
Cited alongside, same era.
‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection ,
C. Fanelli, J. Giroux and Z. Papandreou, · 2022
Cited alongside, same era.
Which metric on the space of collider events? ,
T. Cai, J. Cheng, K. Craig and N. Craig, · 2022
Cited alongside, same era.
Challenges for unsupervised anomaly detection in particle physics ,
K. Fraser, S. Homiller, R. K. Mishra, B. Ostdiek and M. D. Schwartz, · 2022
Cited alongside, same era.
C. Fanelli and J. Giroux, · 2023
Later among the works it cites.
Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection (2023),
T. Finke, M. Hein, G. Kasieczka, M. Krämer, A. Mück, P. Prangchaikul, T. Quadfasel, D. Shih and M. Sommerhalder, · 2023
Later among the works it cites.
The Interplay of Machine Learning–based Resonant Anomaly Detection Methods (2023),
T. Golling, G. Kasieczka, C. Krause, R. Mastandrea, B. Nachman, J. A. Raine, D. Sengupta, D. Shih and M. Sommerhalder, · 2023
Later among the works it cites.
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),
G. Aad et al. , · 2023
Later among the works it cites.
High-dimensional and Permutation Invariant Anomaly Detection (2023),
V. Mikuni and B. Nachman, · 2023
Later among the works it cites.
A spectral metric for collider geometry ,
A. J. Larkoski and J. Thaler, · 2023
Later among the works it cites.
Decorrelation using Optimal Transport (2023),
M. Algren, J. A. Raine and T. Golling, · 2023
Later among the works it cites.
Earth mover’s distance as a measure of CP violation ,
A. Davis, T. Menzo, A. Youssef and J. Zupan, · 2023
Later among the works it cites.
SHAPER: can you hear the shape of a jet? ,
D. Ba, A. S. Dogra, R. Gambhir, A. Tasissa and J. Thaler, · 2023
Later among the works it cites.
Unsupervised learning in the metric space of jets (2023),
T. Gaertner and J. Reiten, · 2023
Later among the works it cites.
Neural embedding: learning the embedding of the manifold of physics data ,
S. E. Park, P. Harris and B. Ostdiek, · 2023
Later among the works it cites.
Anomaly detection under coordinate transformations ,
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee and D. Shih, · 2023
Later among the works it cites.
Efficient approximation of gromov-wasserstein distance using importance sparsification (2023), 2205.13573
M. Li, J. Yu, H. Xu and C. Meng, · 2023
Later among the works it cites.
to appear (2024)
T. Cai, K. Craig, N. Craig and X. Lin, · 2024
Closest in time.