Built on

J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoder
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure
Original
1904
Earlier work this paper cites.
M. Abdughani, J. Ren, L. Wu, J. M. Yang, and J. Zhao, Supervised deep learning in high energy phenomenology: a mini review
Original
1905
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches
Original
1905
Earlier work this paper cites.
J. H. Kim, K. Kong, B. Nachman, and D. Whiteson, The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
Original
1907
Earlier work this paper cites.
S. Wunsch, S. Jórger, R. Wolf, and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Original
1907
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
Original
1909
Earlier work this paper cites.
B. Nachman and C. Shimmin, AI Safety for High Energy Physics
Original
1910
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables
Original
1911
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics
Original
1912
Earlier work this paper cites.
A. Mullin, H. Pacey, M. Parker, M. White, and S. Williams, Does SUSY have friends? A new approach for LHC event analysis
Original
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses
1933
Earlier work this paper cites.
Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
2001
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, PYTHIA 6.4 Physics and Manual
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Earlier work this paper cites.

Similar

T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Cited alongside, same era.
M. Bahr et al., Herwig++ Physics and Manual
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Cited alongside, same era.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012

Then

J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, A Guide to Constraining Effective Field Theories with Machine Learning
Original
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, Constraining Effective Field Theories with Machine Learning
Original
2018
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ExoticsPublicResults
ATLAS Collaboration, Exotic Physics Searches · 2019
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/SupersymmetryPublicResults
ATLAS Collaboration, Supersymmetry searches · 2019
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/HDBSPublicResults
ATLAS Collaboration, Higgs and Diboson Searches · 2019
Later among the works it cites.

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2020

Simulation Assisted Likelihood-free Anomaly Detection

Andreassen, Anders, Nachman, Benjamin, Shih, David

Understand

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program.

A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals.
There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data.
This paper introduces a hybrid method that makes the best of both approaches.

Built on

J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoder
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure
Original
1904
Earlier work this paper cites.
M. Abdughani, J. Ren, L. Wu, J. M. Yang, and J. Zhao, Supervised deep learning in high energy phenomenology: a mini review
Original
1905
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches
Original
1905
Earlier work this paper cites.
J. H. Kim, K. Kong, B. Nachman, and D. Whiteson, The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
Original
1907
Earlier work this paper cites.
S. Wunsch, S. Jórger, R. Wolf, and G. Quast, Reducing the dependence of the neural network function to systematic uncertainties in the input space
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning
Original
1907
Earlier work this paper cites.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers
Original
1908
Earlier work this paper cites.
B. Nachman, A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty
Original
1909
Earlier work this paper cites.
B. Nachman and C. Shimmin, AI Safety for High Energy Physics
Original
1910
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman, and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables
Original
1911
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics
Original
1912
Earlier work this paper cites.
A. Mullin, H. Pacey, M. Parker, M. White, and S. Williams, Does SUSY have friends? A new approach for LHC event analysis
Original
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses
1933
Earlier work this paper cites.
Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
2001
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, PYTHIA 6.4 Physics and Manual
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Earlier work this paper cites.

Similar

T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
Cited alongside, same era.
M. Bahr et al., Herwig++ Physics and Manual
Original
2008
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
Original
2008
Cited alongside, same era.
J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
Cited alongside, same era.
M. Cacciari, G. P. Salam, and G. Soyez, FastJet User Manual
Original
2012

Then

J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, A Guide to Constraining Effective Field Theories with Machine Learning
Original
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, Constraining Effective Field Theories with Machine Learning
Original
2018
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ExoticsPublicResults
ATLAS Collaboration, Exotic Physics Searches · 2019
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/SupersymmetryPublicResults
ATLAS Collaboration, Supersymmetry searches · 2019
Later among the works it cites.
https://twiki.cern.ch/twiki/bin/view/AtlasPublic/HDBSPublicResults
ATLAS Collaboration, Higgs and Diboson Searches · 2019
Later among the works it cites.