Built on

Jack H. Collins, Kiel Howe, and Benjamin Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev. D99
Original
1902
Earlier work this paper cites.
Anders Andreassen, Benjamin Nachman, and David Shih, “Simulation Assisted Likelihood-free Anomaly Detection,” Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Benjamin Nachman and David Shih, “Anomaly Detection with Density Estimation,” Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Torbjorn Sjostrand, Stephen Mrenna, and Peter Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP 05
2006
Earlier work this paper cites.
Suyong Choi, Jaehoon Lim, and Hayoung Oh, “Data-driven Estimation of Background Distribution through Neural Autoregressive Flows,” (2020), arXiv:2008.03636 [hep-ph]
Original
2008
Earlier work this paper cites.
Manuel Bähr, Stefan Gieseke, Martyn A. Gigg, David Grellscheid, Keith Hamilton, Oluseyi Latunde-Dada, Simon Plätzer, Peter Richardson, Michael H. Seymour, Alexander Sherstnev, and Bryan R. Webber, “Herwig++ physics and manual,” The European Physical Journal C 58
2008
Earlier work this paper cites.
Kees Benkendorfer, Luc Le Pottier, and Benjamin Nachman, “Simulation-assisted decorrelation for resonant anomaly detection,” Phys. Rev. D 104
Original
2009
Earlier work this paper cites.
George Stein, Uros Seljak, and Biwei Dai, “Unsupervised in-distribution anomaly detection of new physics through conditional density estimation,” in 34th Conference on Neural Information Processing Systems (2020) arXiv:2012.11638 [cs.LG]
Original
2012
Earlier work this paper cites.
E. G. Tabak and Cristina V. Turner, “A family of nonparametric density estimation algorithms,” Communications on Pure and Applied Mathematics 66
2013
Earlier work this paper cites.
Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,” (2014)
2014
Earlier work this paper cites.

Similar

J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP 02
Original
2014
Cited alongside, same era.
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle, “Made: Masked autoencoder for distribution estimation,” (2015), 10.48550/ARXIV.1502.03509
2015
Cited alongside, same era.
Torbjörn Sjöstrand, Stefan Ask, Jesper R. Christiansen, Richard Corke, Nishita Desai, Philip Ilten, Stephen Mrenna, Stefan Prestel, Christine O. Rasmussen, and Peter Z. Skands, “An introduction to PYTHIA 8.2,” Comput. Phys. Commun. 191
Original
2015
Cited alongside, same era.
Ilya Loshchilov and Frank Hutter, “SGDR: stochastic gradient descent with restarts,” CoRR abs/1608.03983
Original
2016
Cited alongside, same era.

Then

Gregor Kasieczka et al. , “The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics,” Rept. Prog. Phys. 84
Original
2021
Later among the works it cites.
Oz Amram and Cristina Mantilla Suarez, “Tag n’ train: a technique to train improved classifiers on unlabeled data,” Journal of High Energy Physics 2021
2021
Later among the works it cites.
Ivan Kobyzev, Simon J.D. Prince, and Marcus A. Brubaker, “Normalizing flows: An introduction and review of current methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence 43
2021
Later among the works it cites.
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, and David Shih, “Machine learning in the search for new fundamental physics,” Nature Rev. Phys. 4
2022
Closest in time.
Thea Aarrestad et al. , “The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider,” SciPost Phys. 12

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Reading the bibliography…

2022

FETA: Flow-Enhanced Transportation for Anomaly Detection

Golling, Tobias, Klein, Samuel, Mastandrea, Radha et al.

Understand

Resonant anomaly detection is a promising framework for model-independent searches for new particles.

Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions.
We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data.
The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region.

Built on

Jack H. Collins, Kiel Howe, and Benjamin Nachman, “Extending the search for new resonances with machine learning,” Phys. Rev. D99
Original
1902
Earlier work this paper cites.
Anders Andreassen, Benjamin Nachman, and David Shih, “Simulation Assisted Likelihood-free Anomaly Detection,” Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Benjamin Nachman and David Shih, “Anomaly Detection with Density Estimation,” Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
Torbjorn Sjostrand, Stephen Mrenna, and Peter Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP 05
2006
Earlier work this paper cites.
Suyong Choi, Jaehoon Lim, and Hayoung Oh, “Data-driven Estimation of Background Distribution through Neural Autoregressive Flows,” (2020), arXiv:2008.03636 [hep-ph]
Original
2008
Earlier work this paper cites.
Manuel Bähr, Stefan Gieseke, Martyn A. Gigg, David Grellscheid, Keith Hamilton, Oluseyi Latunde-Dada, Simon Plätzer, Peter Richardson, Michael H. Seymour, Alexander Sherstnev, and Bryan R. Webber, “Herwig++ physics and manual,” The European Physical Journal C 58
2008
Earlier work this paper cites.
Kees Benkendorfer, Luc Le Pottier, and Benjamin Nachman, “Simulation-assisted decorrelation for resonant anomaly detection,” Phys. Rev. D 104
Original
2009
Earlier work this paper cites.
George Stein, Uros Seljak, and Biwei Dai, “Unsupervised in-distribution anomaly detection of new physics through conditional density estimation,” in 34th Conference on Neural Information Processing Systems (2020) arXiv:2012.11638 [cs.LG]
Original
2012
Earlier work this paper cites.
E. G. Tabak and Cristina V. Turner, “A family of nonparametric density estimation algorithms,” Communications on Pure and Applied Mathematics 66
2013
Earlier work this paper cites.
Diederik P. Kingma and Jimmy Ba, “Adam: A method for stochastic optimization,” (2014)
2014
Earlier work this paper cites.

Similar

J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi (DELPHES 3), “DELPHES 3, A modular framework for fast simulation of a generic collider experiment,” JHEP 02
Original
2014
Cited alongside, same era.
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle, “Made: Masked autoencoder for distribution estimation,” (2015), 10.48550/ARXIV.1502.03509
2015
Cited alongside, same era.
Torbjörn Sjöstrand, Stefan Ask, Jesper R. Christiansen, Richard Corke, Nishita Desai, Philip Ilten, Stephen Mrenna, Stefan Prestel, Christine O. Rasmussen, and Peter Z. Skands, “An introduction to PYTHIA 8.2,” Comput. Phys. Commun. 191
Original
2015
Cited alongside, same era.
Ilya Loshchilov and Frank Hutter, “SGDR: stochastic gradient descent with restarts,” CoRR abs/1608.03983
Original
2016
Cited alongside, same era.

Then

Gregor Kasieczka et al. , “The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics,” Rept. Prog. Phys. 84
Original
2021
Later among the works it cites.
Oz Amram and Cristina Mantilla Suarez, “Tag n’ train: a technique to train improved classifiers on unlabeled data,” Journal of High Energy Physics 2021
2021
Later among the works it cites.
Ivan Kobyzev, Simon J.D. Prince, and Marcus A. Brubaker, “Normalizing flows: An introduction and review of current methods,” IEEE Transactions on Pattern Analysis and Machine Intelligence 43
2021
Later among the works it cites.
Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, and David Shih, “Machine learning in the search for new fundamental physics,” Nature Rev. Phys. 4
2022
Closest in time.
Thea Aarrestad et al. , “The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider,” SciPost Phys. 12

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Eric M. Metodiev, Benjamin Nachman, and Jesse Thaler, “Classification without labels: Learning from mixed samples in high energy physics,” JHEP 10

Original

2017

Cited alongside, same era.

Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville, “Neural autoregressive flows,” (2018)

2018

Cited alongside, same era.

Jack H. Collins, Kiel Howe, and Benjamin Nachman, “Anomaly Detection for Resonant New Physics with Machine Learning,” Phys. Rev. Lett. 121

Original

2018

Cited alongside, same era.

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., 2019) pp. 8024–8035

2019

Cited alongside, same era.

Gregor Kasieczka, Benjamin Nachman, and David Shih, “Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge (Version v6) [Data set].” (2019), https://doi.org/10.5281/zenodo.4536624

2019

Cited alongside, same era.

Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios, “nflows: normalizing flows in PyTorch,” (2020)

2020

Cited alongside, same era.

Anna Hallin, Joshua Isaacson, Gregor Kasieczka, Claudius Krause, Benjamin Nachman, Tobias Quadfasel, Matthias Schlaffer, David Shih, and Manuel Sommerhalder, “Classifying anomalies through outer density estimation,” Phys. Rev. D 106

Original

Cited in the paper.

Anna Hallin, Gregor Kasieczka, Tobias Quadfasel, David Shih, and Manuel Sommerhalder, “Resonant anomaly detection without background sculpting,” (2022b), arXiv:2210.14924 [hep-ph]

Original

Cited in the paper.

Original

2022

Closest in time.

“Evidence for the simultaneous production of four top quarks in proton-proton collisions at s \sqrt{s} = 13 TeV,” (2022)

2022

Closest in time.

John Andrew Raine, Samuel Klein, Debajyoti Sengupta, and Tobias Golling, “CURTAINs for your Sliding Window: Constructing Unobserved Regions by Transforming Adjacent Intervals,” (2022), arXiv:2203.09470 [hep-ph]

Original

2022

Closest in time.

Samuel Klein, John Andrew Raine, and Tobias Golling, “Flows for flows: Training normalizing flows between arbitrary distributions with maximum likelihood estimation,” (2022)

2022

Closest in time.

Radha Mastandrea and Benjamin Nachman, “Efficiently Moving Instead of Reweighting Collider Events with Machine Learning,” in 36th Conference on Neural Information Processing Systems (2022) arXiv:2212.06155 [hep-ph]

Original

2022

Closest in time.