Built on

J. H. Collins, K. Howe, and B. Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
P. T. Komiske, E. M. Metodiev, and J. Thaler, “Metric Space of Collider Events,” Phys. Rev. Lett
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, “A robust anomaly finder based on autoencoder,” arXiv:1903.02032 [hep-ph]
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, “Uncovering latent jet substructure,” Phys. Rev. D
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, “Adversarially-trained autoencoders for robust unsupervised new physics searches,” JHEP
Original
1905
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, “Mass Agnostic Jet Taggers,” SciPost Phys
Original
1908
Earlier work this paper cites.
11, 2019
Original
T. Cheng, “Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider,” in 33rd Annual Conference on Neural Information Processing Systems · 1911
Earlier work this paper cites.
B. Nachman and D. Shih, “Anomaly Detection with Density Estimation,” arXiv:2001.04990 [hep-ph]
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, “Simulation Assisted Likelihood-free Anomaly Detection,” Phys. Rev. D
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, “DisCo Fever: Robust Networks Through Distance Correlation,” arXiv:2001.05310 [hep-ph]
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, “Tag N’ Train: A Technique to Train Improved Classifiers on Unlabeled Data,” arXiv:2002.12376 [hep-ph]
Original
2002
Earlier work this paper cites.
V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial Intelligence Review
2004
Earlier work this paper cites.

Similar

B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc, “Learning the latent structure of collider events,” arXiv:2005.12319 [hep-ph]
Original
2005
Cited alongside, same era.
M. C. Romao, N. Castro, and R. Pedro, “Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders,” arXiv:2006.05432 [hep-ph]
Original
2006
Cited alongside, same era.
T. Sjöstrand, S. Mrenna, and P. Skands, “A brief introduction to pythia 8.1,” Computer Physics Communications
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti-kt jet clustering algorithm,” Journal of High Energy Physics
2008
Cited alongside, same era.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research
2008
Cited alongside, same era.

Then

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2014
2014
Later among the works it cites.
C. Doersch, “Tutorial on Variational Autoencoders,” arXiv e-prints
Original
2016
Later among the works it cites.
J. A. Aguilar-Saavedra, J. H. Collins, and R. K. Mishra, “A generic anti-QCD jet tagger,” JHEP
Original
2017
Later among the works it cites.
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, “Decorrelated Jet Substructure Tagging using Adversarial Neural Networks,” Phys. Rev. D
Original
2017
Later among the works it cites.
J. H. Collins, K. Howe, and B. Nachman, “Anomaly Detection for Resonant New Physics with Machine Learning,” Phys. Rev. Lett
Original

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2020

Variational Autoencoders for Anomalous Jet Tagging

Cheng, Taoli, Arguin, Jean-François, Leissner-Martin, Julien et al.

Understand

We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider.

By taking in low-level jet constituents' information, and training with background QCD jets in an unsupervised manner, the VAE is able to encode important information for reconstructing jets, while learning an expressive posterior distribution in the latent space.
When using the VAE as an anomaly detector, we present different approaches to detect anomalies: directly comparing in the input space or, instead, working in the latent space.
In order to facilitate general search approaches such as bump-hunt, mass-decorrelated VAEs based on distance correlation regularization are also studied.

Built on

J. H. Collins, K. Howe, and B. Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
P. T. Komiske, E. M. Metodiev, and J. Thaler, “Metric Space of Collider Events,” Phys. Rev. Lett
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, “A robust anomaly finder based on autoencoder,” arXiv:1903.02032 [hep-ph]
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, “Uncovering latent jet substructure,” Phys. Rev. D
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, “Adversarially-trained autoencoders for robust unsupervised new physics searches,” JHEP
Original
1905
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, “Mass Agnostic Jet Taggers,” SciPost Phys
Original
1908
Earlier work this paper cites.
11, 2019
Original
T. Cheng, “Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider,” in 33rd Annual Conference on Neural Information Processing Systems · 1911
Earlier work this paper cites.
B. Nachman and D. Shih, “Anomaly Detection with Density Estimation,” arXiv:2001.04990 [hep-ph]
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, “Simulation Assisted Likelihood-free Anomaly Detection,” Phys. Rev. D
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, “DisCo Fever: Robust Networks Through Distance Correlation,” arXiv:2001.05310 [hep-ph]
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, “Tag N’ Train: A Technique to Train Improved Classifiers on Unlabeled Data,” arXiv:2002.12376 [hep-ph]
Original
2002
Earlier work this paper cites.
V. J. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial Intelligence Review
2004
Earlier work this paper cites.

Similar

B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc, “Learning the latent structure of collider events,” arXiv:2005.12319 [hep-ph]
Original
2005
Cited alongside, same era.
M. C. Romao, N. Castro, and R. Pedro, “Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders,” arXiv:2006.05432 [hep-ph]
Original
2006
Cited alongside, same era.
T. Sjöstrand, S. Mrenna, and P. Skands, “A brief introduction to pythia 8.1,” Computer Physics Communications
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, “The anti-kt jet clustering algorithm,” Journal of High Energy Physics
2008
Cited alongside, same era.
L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research
2008
Cited alongside, same era.

Then

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” 2014
2014
Later among the works it cites.
C. Doersch, “Tutorial on Variational Autoencoders,” arXiv e-prints
Original
2016
Later among the works it cites.
J. A. Aguilar-Saavedra, J. H. Collins, and R. K. Mishra, “A generic anti-QCD jet tagger,” JHEP
Original
2017
Later among the works it cites.
C. Shimmin, P. Sadowski, P. Baldi, E. Weik, D. Whiteson, E. Goul, and A. Søgaard, “Decorrelated Jet Substructure Tagging using Adversarial Neural Networks,” Phys. Rev. D
Original
2017
Later among the works it cites.
J. H. Collins, K. Howe, and B. Nachman, “Anomaly Detection for Resonant New Physics with Machine Learning,” Phys. Rev. Lett
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

G. J. Székely, M. L. Rizzo, and N. K. Bakirov, “Measuring and testing dependence by correlation of distances,” arXiv e-prints

Original

2008

Cited alongside, same era.

V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surv

2009

Cited alongside, same era.

J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, “Madgraph 5: going beyond,” Journal of High Energy Physics

2011

Cited alongside, same era.

G. Branco, P. Ferreira, L. Lavoura, M. Rebelo, M. Sher, and J. P. Silva, “Theory and phenomenology of two-Higgs-doublet models,” Phys. Rept

Original

2012

Cited alongside, same era.

D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” arXiv e-prints

Original

2013

Cited alongside, same era.

M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko, “A review of novelty detection,” Signal Processing

2014

Cited alongside, same era.

J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi, “Delphes 3: a modular framework for fast simulation of a generic collider experiment,” Journal of High Energy Physics

2014

Cited alongside, same era.

2018

Later among the works it cites.

E. Nalisnick, A. Matsukawa, Y. Whye Teh, D. Gorur, and B. Lakshminarayanan, “Do Deep Generative Models Know What They Don’t Know?,” arXiv e-prints

Original

2018

Later among the works it cites.

D. Hendrycks, M. Mazeika, and T. Dietterich, “Deep Anomaly Detection with Outlier Exposure,” arXiv e-prints

Original

2018

Later among the works it cites.