Built on

C. A. Arguelles, A. Schneider, and T. Yuan, “A binned likelihood for stochastic models,” JHEP
Original
1901
Earlier work this paper cites.
F. F. Freitas, C. K. Khosa, and V. Sanz, “Exploring the standard model EFT in VH production with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
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.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, “MadMiner: Machine learning-based inference for particle physics,” arXiv:1907.10621 [hep-ph]
Original
1907
Earlier work this paper cites.
J. Brehmer, S. Dawson, S. Homiller, F. Kling, and T. Plehn, “Benchmarking simplified template cross sections in W H WH production,” JHEP
Original
1908
Earlier work this paper cites.
A. Alloul, N. D. Christensen, C. Degrande, C. Duhr, and B. Fuks, “FeynRules 2.0 - A complete toolbox for tree-level phenomenology,” Comput. Phys. Commun
Original
1921
Earlier work this paper cites.
J. Neyman and E. S. Pearson, “On the problem of the most efficient tests of statistical hypotheses,” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character
1933
Earlier work this paper cites.
S. S. Wilks, “The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses,” Annals Math. Statist
1938
Earlier work this paper cites.
A. Wald, “Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large,” Trans. Am. Math. Soc
1943
Earlier work this paper cites.
M. Shilling, “Multivariate Two-Sample Tests Based on Neares Neighbors,” Journal of the American Statistical Association
1986
Earlier work this paper cites.
N. Henze, “A multivariate two-sample test based on the number of nearest neighbor type coincidences,” Ann. Statist
1988
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems
1989
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw
1989
Earlier work this paper cites.
V. Y. Kreinovich, “Arbitrary nonlinearity is sufficient to represent all functions by neural networks: A theorem,” Neural Networks
1991
Earlier work this paper cites.
S. Forte, L. Garrido, J. I. Latorre, and A. Piccione, “Neural network parametrization of deep inelastic structure functions,” JHEP
2002
Earlier work this paper cites.
Q. Wang, S. Kulkarni, and S. Verdú, “Divergence estimation of continuous distributions based on data-dependent partitions,” IEEE Transactions on Information Theory
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP
2006
Earlier work this paper cites.
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, “MadGraph 5 : Going Beyond,” JHEP
Original
2011
Earlier work this paper cites.
G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics,” Eur. Phys. J
Original
2013
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, “Playing Tag with ANN: Boosted Top Identification with Pattern Recognition,” JHEP
Original
2015
Earlier work this paper cites.
M. Abadi et al
2015
Earlier work this paper cites.

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S. Liang and R. Srikant, “Why Deep Neural Networks for Function Approximation?,” ArXiv e-prints
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2016
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L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images − - deep learning edition,” JHEP
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A. Schwartzman, M. Kagan, L. Mackey, B. Nachman, and L. De Oliveira, “Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events,” J. Phys. Conf. Ser
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Then

S. Macaluso and D. Shih, “Pulling out all the tops with computer vision and deep learning,” Journal of High Energy Physics
2018
Later among the works it cites.
S. Chang, T. Cohen, and B. Ostdiek, “What is the machine learning?,” Physical Review D
2018
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, “(Machine) Learning to Do More with Less,” JHEP
Original
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “A guide to constraining effective field theories with machine learning,” Physical Review D
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “Constraining effective field theories with machine learning,” Physical Review Letters
2018
Later among the works it cites.

Beyond the bibliography

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

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Reading the bibliography…

2019

Learning Multivariate New Physics

D'Agnolo, Raffaele Tito, Grosso, Gaia, Pierini, Maurizio et al.

Understand

We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets.

Our data analysis strategy does not rely on any prior assumption on the nature of the deviation.
It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model.
The main conceptual building blocks were introduced in Ref.

Built on

C. A. Arguelles, A. Schneider, and T. Yuan, “A binned likelihood for stochastic models,” JHEP
Original
1901
Earlier work this paper cites.
F. F. Freitas, C. K. Khosa, and V. Sanz, “Exploring the standard model EFT in VH production with machine learning,” Phys. Rev
Original
1902
Earlier work this paper cites.
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.
J. Brehmer, F. Kling, I. Espejo, and K. Cranmer, “MadMiner: Machine learning-based inference for particle physics,” arXiv:1907.10621 [hep-ph]
Original
1907
Earlier work this paper cites.
J. Brehmer, S. Dawson, S. Homiller, F. Kling, and T. Plehn, “Benchmarking simplified template cross sections in W H WH production,” JHEP
Original
1908
Earlier work this paper cites.
A. Alloul, N. D. Christensen, C. Degrande, C. Duhr, and B. Fuks, “FeynRules 2.0 - A complete toolbox for tree-level phenomenology,” Comput. Phys. Commun
Original
1921
Earlier work this paper cites.
J. Neyman and E. S. Pearson, “On the problem of the most efficient tests of statistical hypotheses,” Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character
1933
Earlier work this paper cites.
S. S. Wilks, “The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses,” Annals Math. Statist
1938
Earlier work this paper cites.
A. Wald, “Tests of Statistical Hypotheses Concerning Several Parameters When the Number of Observations is Large,” Trans. Am. Math. Soc
1943
Earlier work this paper cites.
M. Shilling, “Multivariate Two-Sample Tests Based on Neares Neighbors,” Journal of the American Statistical Association
1986
Earlier work this paper cites.
N. Henze, “A multivariate two-sample test based on the number of nearest neighbor type coincidences,” Ann. Statist
1988
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems
1989
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw
1989
Earlier work this paper cites.
V. Y. Kreinovich, “Arbitrary nonlinearity is sufficient to represent all functions by neural networks: A theorem,” Neural Networks
1991
Earlier work this paper cites.
S. Forte, L. Garrido, J. I. Latorre, and A. Piccione, “Neural network parametrization of deep inelastic structure functions,” JHEP
2002
Earlier work this paper cites.
Q. Wang, S. Kulkarni, and S. Verdú, “Divergence estimation of continuous distributions based on data-dependent partitions,” IEEE Transactions on Information Theory
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna, and P. Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP
2006
Earlier work this paper cites.
J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, “MadGraph 5 : Going Beyond,” JHEP
Original
2011
Earlier work this paper cites.
G. Cowan, K. Cranmer, E. Gross, and O. Vitells, “Asymptotic formulae for likelihood-based tests of new physics,” Eur. Phys. J
Original
2013
Earlier work this paper cites.
DELPHES 3
Original
2014
Earlier work this paper cites.
L. G. Almeida, M. Backović, M. Cliche, S. J. Lee, and M. Perelstein, “Playing Tag with ANN: Boosted Top Identification with Pattern Recognition,” JHEP
Original
2015
Earlier work this paper cites.
M. Abadi et al
2015
Earlier work this paper cites.

Similar

S. Liang and R. Srikant, “Why Deep Neural Networks for Function Approximation?,” ArXiv e-prints
Original
2016
Cited alongside, same era.
L. de Oliveira, M. Kagan, L. Mackey, B. Nachman, and A. Schwartzman, “Jet-images − - deep learning edition,” JHEP
Original
2016
Cited alongside, same era.
A. Schwartzman, M. Kagan, L. Mackey, B. Nachman, and L. De Oliveira, “Image Processing, Computer Vision, and Deep Learning: new approaches to the analysis and physics interpretation of LHC events,” J. Phys. Conf. Ser
2016
Cited alongside, same era.
M. Kagan, L. d. Oliveira, L. Mackey, B. Nachman, and A. Schwartzman, “Boosted Jet Tagging with Jet-Images and Deep Neural Networks,” EPJ Web Conf
2016
Cited alongside, same era.
P. Baldi, K. Bauer, C. Eng, P. Sadowski, and D. Whiteson, “Jet Substructure Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev

Then

S. Macaluso and D. Shih, “Pulling out all the tops with computer vision and deep learning,” Journal of High Energy Physics
2018
Later among the works it cites.
S. Chang, T. Cohen, and B. Ostdiek, “What is the machine learning?,” Physical Review D
2018
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, “(Machine) Learning to Do More with Less,” JHEP
Original
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “A guide to constraining effective field theories with machine learning,” Physical Review D
2018
Later among the works it cites.
J. Brehmer, K. Cranmer, G. Louppe, and J. Pavez, “Constraining effective field theories with machine learning,” Physical Review Letters
2018
Later among the works it cites.

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

2016

Cited alongside, same era.

D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, “Jet Flavor Classification in High-Energy Physics with Deep Neural Networks,” Phys. Rev

Original

2016

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P. Baldi, K. Cranmer, T. Faucett, P. Sadowski, and D. Whiteson, “Parameterized neural networks for high-energy physics,” Eur. Phys. J

Original

2016

Cited alongside, same era.

P. Asadi, M. R. Buckley, A. DiFranzo, A. Monteux, and D. Shih, “Digging Deeper for New Physics in the LHC Data,” JHEP

Original

2017

Cited alongside, same era.

H. Montanelli and Q. Du, “Deep ReLU networks lessen the curse of dimensionality,” ArXiv e-prints

Original

2017

Cited alongside, same era.

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

Original

2017

Cited alongside, same era.

J. Barnard, E. N. Dawe, M. J. Dolan, and N. Rajcic, “Parton Shower Uncertainties in Jet Substructure Analyses with Deep Neural Networks,” Phys. Rev

Original

2017

Cited alongside, same era.

G. Kasieczka, T. Plehn, M. Russell, and T. Schell, “Deep-learning Top Taggers or The End of QCD?,” JHEP

Original

2017

Cited alongside, same era.

M. Paganini, L. de Oliveira, and B. Nachman, “CaloGAN : Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks,” Phys. Rev

Original

2018

Later among the works it cites.

M. Paganini, L. de Oliveira, and B. Nachman, “Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters,” Phys. Rev. Lett

Original

2018

Later among the works it cites.

R. T. D’Agnolo and A. Wulzer, “Learning New Physics from a Machine,” Phys. Rev

Original

2019

Closest in time.

J. H. Collins, K. Howe, and B. Nachman, “Extending the search for new resonances with machine learning,” Physical Review D

2019

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A. Blance, M. Spannowsky, and P. Waite, “Adversarially-trained autoencoders for robust unsupervised new physics searches,” Journal of High Energy Physics

2019

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

Original

2019

Closest in time.

O. Cerri, T. Q. Nguyen, M. Pierini, M. Spiropulu, and J.-R. Vlimant, “Variational Autoencoders for New Physics Mining at the Large Hadron Collider,” JHEP

Original

2019

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A. De Simone and T. Jacques, “Guiding New Physics Searches with Unsupervised Learning,” Eur. Phys. J

Original

2019

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A. J. Larkoski, I. Moult, and B. Nachman, “Jet substructure at the large hadron collider: A review of recent advances in theory and machine learning,” Physics Reports

2019

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A. Andreassen, I. Feige, C. Frye, and M. D. Schwartz, “Junipr: a framework for unsupervised machine learning in particle physics,” The European Physical Journal C

2019

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A. Andreassen, B. Nachman, and D. Shih, “Simulation assisted likelihood-free anomaly detection,” Physical Review D

2020

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M. Farina, Y. Nakai, and D. Shih, “Searching for new physics with deep autoencoders,” Physical Review D

2020

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J. Hajer, Y.-Y. Li, T. Liu, and H. Wang, “Novelty detection meets collider physics,” Physical Review D

2020

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J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer, “Mining gold from implicit models to improve likelihood-free inference,” Proceedings of the National Academy of Sciences

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G. Kasieczka, B. Nachman, and D. Shih, “Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge,”. https://doi.org/10.5281/zenodo.3596919

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