Built on

J. H. Collins, K. Howe and B. Nachman, Extending the search for new resonances with machine learning , Phys. Rev. D99
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoder , 1903.02032
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy and J. F. Kamenik, Uncovering latent jet substructure , Phys. Rev. D100
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches , JHEP 10
Original
1905
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate and B. Ostdiek, Mass Agnostic Jet Taggers , 1908.08959
Original
1908
Earlier work this paper cites.
D. Choi, C. J. Shallue, Z. Nado, J. Lee, C. J. Maddison and G. E. Dahl, On empirical comparisons of optimizers for deep learning , CoRR abs/1910.05446
Original
1910
Earlier work this paper cites.
E. Alvarez, F. Lamagna and M. Szewc, Topic Model for four-top at the LHC , JHEP 01
Original
1911
Earlier work this paper cites.
A. Andreassen, B. Nachman and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection , 2001.05001
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , 2001.04990
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation , 2001.05310
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 , 2002.12376
Original
2002
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, M. I. Jordan and J. Lafferty, Latent dirichlet allocation , Journal of Machine Learning Research 3
2003
Earlier work this paper cites.
ATLAS Collaboration, Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector , 2005.02983
Original
2005
Earlier work this paper cites.

Similar

O. Knapp, G. Dissertori, O. Cerri, T. Q. Nguyen, J.-R. Vlimant and M. Pierini, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark , 2005.01598
Original
2005
Cited alongside, same era.
B. M. Dillon, D. A. Faroughy, J. F. Kamenik and M. Szewc, Learning the latent structure of collider events , JHEP 10
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 , 2006.05432
Original
2006
Cited alongside, same era.
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging , 2007.01850
Original
2007
Cited alongside, same era.
C. K. Khosa and V. Sanz, Anomaly Awareness , 2007.14462

Then

J. Dolen, P. Harris, S. Marzani, S. Rappoccio and N. Tran, Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , JHEP 05
Original
2016
Later among the works it cites.
E. M. Metodiev, B. Nachman and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics , JHEP 10
Original
2017
Later among the works it cites.
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, A generic anti-QCD jet tagger , JHEP 11
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. 121
Original
2018
Later among the works it cites.
E. M. Metodiev and J. Thaler, Jet Topics: Disentangling Quarks and Gluons at Colliders , Phys. Rev. Lett. 120

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2021

Bump Hunting in Latent Space

Bortolato, Blaž, Dillon, Barry M., Kamenik, Jernej F. et al.

Understand

Unsupervised anomaly detection could be crucial in future analyses searching for rare phenomena in large datasets, as for example collected at the LHC.

To this end, we introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets.
We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and characterise features in measured spectra as caused by the presence of anomalies in a dataset.

Built on

J. H. Collins, K. Howe and B. Nachman, Extending the search for new resonances with machine learning , Phys. Rev. D99
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoder , 1903.02032
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy and J. F. Kamenik, Uncovering latent jet substructure , Phys. Rev. D100
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches , JHEP 10
Original
1905
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate and B. Ostdiek, Mass Agnostic Jet Taggers , 1908.08959
Original
1908
Earlier work this paper cites.
D. Choi, C. J. Shallue, Z. Nado, J. Lee, C. J. Maddison and G. E. Dahl, On empirical comparisons of optimizers for deep learning , CoRR abs/1910.05446
Original
1910
Earlier work this paper cites.
E. Alvarez, F. Lamagna and M. Szewc, Topic Model for four-top at the LHC , JHEP 01
Original
1911
Earlier work this paper cites.
A. Andreassen, B. Nachman and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection , 2001.05001
Original
2001
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , 2001.04990
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation , 2001.05310
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 , 2002.12376
Original
2002
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, M. I. Jordan and J. Lafferty, Latent dirichlet allocation , Journal of Machine Learning Research 3
2003
Earlier work this paper cites.
ATLAS Collaboration, Dijet resonance search with weak supervision using 13 TeV pp collisions in the ATLAS detector , 2005.02983
Original
2005
Earlier work this paper cites.

Similar

O. Knapp, G. Dissertori, O. Cerri, T. Q. Nguyen, J.-R. Vlimant and M. Pierini, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark , 2005.01598
Original
2005
Cited alongside, same era.
B. M. Dillon, D. A. Faroughy, J. F. Kamenik and M. Szewc, Learning the latent structure of collider events , JHEP 10
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 , 2006.05432
Original
2006
Cited alongside, same era.
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational Autoencoders for Anomalous Jet Tagging , 2007.01850
Original
2007
Cited alongside, same era.
C. K. Khosa and V. Sanz, Anomaly Awareness , 2007.14462

Then

J. Dolen, P. Harris, S. Marzani, S. Rappoccio and N. Tran, Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure , JHEP 05
Original
2016
Later among the works it cites.
E. M. Metodiev, B. Nachman and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics , JHEP 10
Original
2017
Later among the works it cites.
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, A generic anti-QCD jet tagger , JHEP 11
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. 121
Original
2018
Later among the works it cites.
E. M. Metodiev and J. Thaler, Jet Topics: Disentangling Quarks and Gluons at Colliders , Phys. Rev. Lett. 120

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Original

2007

Cited alongside, same era.

S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. W. Toomey, E. Usai et al., Decoding Dark Matter Substructure without Supervision , 2008.12731

Original

2008

Cited alongside, same era.

K. Benkendorfer, L. L. Pottier and B. Nachman, Simulation-Assisted Decorrelation for Resonant Anomaly Detection , 2009.02205

Original

2009

Cited alongside, same era.

V. Mikuni and F. Canelli, UCluster: Unsupervised clustering for collider physics , 2010.07106

Original

2010

Cited alongside, same era.

M. van Beekveld, S. Caron, L. Hendriks, P. Jackson, A. Leinweber, S. Otten et al., Combining outlier analysis algorithms to identify new physics at the LHC , 2010.07940

Original

2010

Cited alongside, same era.

S. E. Park, D. Rankin, S.-M. Udrescu, M. Yunus and P. Harris, Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge , 2011.03550

Original

2011

Cited alongside, same era.

J. Thaler and K. Van Tilburg, Identifying boosted objects with n-subjettiness , Journal of High Energy Physics 2011

2011

Cited alongside, same era.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel et al., Scikit-learn: Machine learning in Python , Journal of Machine Learning Research 12

2011

Cited alongside, same era.

Original

2018

Later among the works it cites.

I. Moult, B. Nachman and D. Neill, Convolved Substructure: Analytically Decorrelating Jet Substructure Observables , JHEP 05

Original

2018

Later among the works it cites.

T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, QCD or What? , SciPost Phys. 6

Original

2019