Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
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.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
arXiv:1903.11788
Original
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin, and A. Volokhova, Cherenkov Detectors Fast Simulation Using Neural Networks · 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. S. Albergo, G. Kanwar, and P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory
Original
1904
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. A. Aguilar-Saavedra and F. R. Joaquim, The minimal stealth boson: models and benchmarks
Original
1905
Earlier work this paper cites.
A. Andreassen, I. Feige, C. Frye, and M. D. Schwartz, Binary JUNIPR: an interpretable probabilistic model for discrimination
Original
1906
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, Neural spline flows
Original
1906
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.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
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.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers
Original
1908
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks
Original
1909
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.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
arXiv:1912.02748
Original
J. Arjona Martinez, T. Q. Nguyen, M. Pierini, M. Spiropulu, and J.-R. Vlimant, Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description · 1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
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.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
Original
2001
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation
2003
Earlier work this paper cites.
PhD thesis, Wisconsin U., Madison, 2005
K. S. Cranmer, Searching for new physics: Contributions to LEP and the LHC · 2005
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.

Similar

M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
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T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
Original
2008
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M. Cacciari, G. P. Salam, and G. Soyez, The anti- k t k_{t} jet clustering algorithm
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2008
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J. Thaler and K. Van Tilburg, Identifying Boosted Objects with N-subjettiness
Original
2011
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Original
2012
Cited alongside, same era.

Then

M. Erdmann, L. Geiger, J. Glombitza, and D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks
Original
2018
Later among the works it cites.
J. W. Monk, Deep Learning as a Parton Shower
Original
2018
Later among the works it cites.
K. Agashe, J. H. Collins, P. Du, S. Hong, D. Kim, and R. K. Mishra, Detecting a Boosted Diboson Resonance
Original
2018
Later among the works it cites.
I. Moult, B. Nachman, and D. Neill, Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
Original
2018
Later among the works it cites.
C. Huang, D. Krueger, A. Lacoste, and A. C. Courville, Neural autoregressive flows

Beyond the bibliography

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Reading the bibliography…

2020

Anomaly Detection with Density Estimation

Nachman, Benjamin, Shih, David

Understand

We leverage recent breakthroughs in neural density estimation to propose a new unsupervised anomaly detection technique (ANODE).

By estimating the probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a likelihood ratio of data vs.
background can be constructed.
This likelihood ratio is broadly sensitive to overdensities in the data that could be due to localized anomalies.

Built on

B. Hashemi, N. Amin, K. Datta, D. Olivito, and M. Pierini, LHC analysis-specific datasets with Generative Adversarial Networks
Original
1901
Earlier work this paper cites.
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.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
Original
1903
Earlier work this paper cites.
R. Di Sipio, M. Faucci Giannelli, S. Ketabchi Haghighat, and S. Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
Original
1903
Earlier work this paper cites.
arXiv:1903.11788
Original
D. Derkach, N. Kazeev, F. Ratnikov, A. Ustyuzhanin, and A. Volokhova, Cherenkov Detectors Fast Simulation Using Neural Networks · 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. S. Albergo, G. Kanwar, and P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory
Original
1904
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. A. Aguilar-Saavedra and F. R. Joaquim, The minimal stealth boson: models and benchmarks
Original
1905
Earlier work this paper cites.
A. Andreassen, I. Feige, C. Frye, and M. D. Schwartz, Binary JUNIPR: an interpretable probabilistic model for discrimination
Original
1906
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, Neural spline flows
Original
1906
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.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN LHC Events
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.
L. Bradshaw, R. K. Mishra, A. Mitridate, and B. Ostdiek, Mass Agnostic Jet Taggers
Original
1908
Earlier work this paper cites.
S. Carrazza and F. A. Dreyer, Lund jet images from generative and cycle-consistent adversarial networks
Original
1909
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.
A. Butter, T. Plehn, and R. Winterhalder, How to GAN Event Subtraction
Original
1912
Earlier work this paper cites.
arXiv:1912.02748
Original
J. Arjona Martinez, T. Q. Nguyen, M. Pierini, M. Spiropulu, and J.-R. Vlimant, Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description · 1912
Earlier work this paper cites.
M. Bellagente, A. Butter, G. Kasieczka, T. Plehn, and R. Winterhalder, How to GAN away Detector Effects
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.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting
1958
Earlier work this paper cites.
Ph.D. thesis, University of California at Berkeley (2000)
B. Knuteson · 2000
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
Original
2001
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
Original
2001
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, Latent dirichlet allocation
2003
Earlier work this paper cites.
PhD thesis, Wisconsin U., Madison, 2005
K. S. Cranmer, Searching for new physics: Contributions to LEP and the LHC · 2005
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.

Similar

M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder
2006
Cited alongside, same era.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1
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
Cited alongside, same era.

Then

M. Erdmann, L. Geiger, J. Glombitza, and D. Schmidt, Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks
Original
2018
Later among the works it cites.
J. W. Monk, Deep Learning as a Parton Shower
Original
2018
Later among the works it cites.
K. Agashe, J. H. Collins, P. Du, S. Hong, D. Kim, and R. K. Mishra, Detecting a Boosted Diboson Resonance
Original
2018
Later among the works it cites.
I. Moult, B. Nachman, and D. Neill, Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
Original
2018
Later among the works it cites.
C. Huang, D. Krueger, A. Lacoste, and A. C. Courville, Neural autoregressive flows

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness

Original

2012

Cited alongside, same era.

J. Stevens and M. Williams, uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers

Original

2013

Cited alongside, same era.

DELPHES 3

Original

2014

Cited alongside, same era.

M. Selvaggi, DELPHES 3: A modular framework for fast-simulation of generic collider experiments

2014

Cited alongside, same era.

D. Rezende and S. Mohamed, Variational inference with normalizing flows

2015

Cited alongside, same era.

M. Germain, K. Gregor, I. Murray, and H. Larochelle, Made: Masked autoencoder for distribution estimation

2015

Cited alongside, same era.

A. Mertens, New features in Delphes 3

2015

Cited alongside, same era.

Original

2018