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J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D99
Original
1902
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, “Neural spline flows,” (2019), arXiv:1906.04032 [stat.ML]
Original
1906
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” (2019), arXiv:1912.01703 [cs.LG]
Original
1912
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, (2020), 10.1007/JHEP01(2021)153 , arXiv:2002.12376 [hep-ph]
Original
2002
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, Journal of High Energy Physics 2006
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Physics Letters B 641
2006
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, Computer Physics Communications 178
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, Journal of High Energy Physics 2008
2008
Earlier work this paper cites.
K. Benkendorfer, L. L. Pottier, and B. Nachman, (2020), arXiv:2009.02205 [hep-ph]
Original
2009
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Journal of High Energy Physics 2011
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and Édouard Duchesnay, Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
G. Stein, U. Seljak, and B. Dai, in 34th Conference on Neural Information Processing Systems (2020) arXiv:2012.11638 [cs.LG]
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The European Physical Journal C 72
2012
Earlier work this paper cites.

Similar

J. Thaler and K. Van Tilburg, Journal of High Energy Physics 2012
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L. Dinh, D. Krueger, and Y. Bengio, “Nice: Non-linear independent components estimation,” (2015), arXiv:1410.8516 [cs.LG]
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2015
Cited alongside, same era.

Then

D. Shih, “Additional QCD Background Events for LHCO2020 R&D (signal region only),” (2021)
2021
Later among the works it cites.
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, and D. Shih, Nature Rev. Phys. 4
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Later among the works it cites.
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih, and M. Sommerhalder, Physical Review D 106
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Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih, “R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge,” (2022)
2022
Later among the works it cites.
LHCb Collaboration, “Publications of the QCD, Electroweak and Exotica Working Group,” (2023), http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/Summary_QEE.html
2023
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2023

Residual ANODE

Das, Ranit, Kasieczka, Gregor, Shih, David

Understand

We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability.

The key to R-ANODE is to enhance the inductive bias of the anomaly detection task by fitting a normalizing flow directly to the small and unknown signal component, while holding fixed a background model (also a normalizing flow) learned from sidebands.
In doing so, R-ANODE is able to outperform all classifier-based, weakly-supervised approaches, as well as the previous ANODE method which fit a density estimator to all of the data in the signal region instead of just the signal.
We show that the method works equally well whether the unknown signal fraction is learned or fixed, and is even robust to signal fraction misspecification.

Built on

J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D99
Original
1902
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios, “Neural spline flows,” (2019), arXiv:1906.04032 [stat.ML]
Original
1906
Earlier work this paper cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” (2019), arXiv:1912.01703 [cs.LG]
Original
1912
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, (2020), 10.1007/JHEP01(2021)153 , arXiv:2002.12376 [hep-ph]
Original
2002
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, Journal of High Energy Physics 2006
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Physics Letters B 641
2006
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Skands, Computer Physics Communications 178
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, Journal of High Energy Physics 2008
2008
Earlier work this paper cites.
K. Benkendorfer, L. L. Pottier, and B. Nachman, (2020), arXiv:2009.02205 [hep-ph]
Original
2009
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Journal of High Energy Physics 2011
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and Édouard Duchesnay, Journal of Machine Learning Research 12
2011
Earlier work this paper cites.
G. Stein, U. Seljak, and B. Dai, in 34th Conference on Neural Information Processing Systems (2020) arXiv:2012.11638 [cs.LG]
Original
2012
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, The European Physical Journal C 72
2012
Earlier work this paper cites.

Similar

J. Thaler and K. Van Tilburg, Journal of High Energy Physics 2012
2012
Cited alongside, same era.
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaître, A. Mertens, and M. Selvaggi, Journal of High Energy Physics 2014
2014
Cited alongside, same era.
M. Selvaggi, Journal of Physics: Conference Series 523
2014
Cited alongside, same era.
A. Mertens, Journal of Physics: Conference Series 608
2015
Cited alongside, same era.
L. Dinh, D. Krueger, and Y. Bengio, “Nice: Non-linear independent components estimation,” (2015), arXiv:1410.8516 [cs.LG]
Original
2015
Cited alongside, same era.

Then

D. Shih, “Additional QCD Background Events for LHCO2020 R&D (signal region only),” (2021)
2021
Later among the works it cites.
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, and D. Shih, Nature Rev. Phys. 4
2022
Later among the works it cites.
A. Hallin, J. Isaacson, G. Kasieczka, C. Krause, B. Nachman, T. Quadfasel, M. Schlaffer, D. Shih, and M. Sommerhalder, Physical Review D 106
2022
Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih, “R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge,” (2022)
2022
Later among the works it cites.
LHCb Collaboration, “Publications of the QCD, Electroweak and Exotica Working Group,” (2023), http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/Summary_QEE.html
2023
Closest in time.

Beyond the bibliography

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G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, in Advances in Neural Information Processing Systems , Vol. 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Curran Associates, Inc., 2017)

2017

Cited alongside, same era.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” (2017), arXiv:1412.6980 [cs.LG]

Original

2017

Cited alongside, same era.

J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. Lett. 121

Original

2018

Cited alongside, same era.

G. Papamakarios, T. Pavlakou, and I. Murray, “Masked autoregressive flow for density estimation,” (2018), arXiv:1705.07057 [stat.ML]

Original