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J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D99
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1902
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A. Andreassen, B. Nachman, and D. Shih, Phys. Rev. D 101
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M. Cacciari and G. P. Salam, Phys. Lett. B641
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T. Sjöstrand, S. Mrenna, and P. Z. Skands, Comput. Phys. Commun. 178
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2008
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M. Cacciari, G. P. Salam, and G. Soyez, JHEP 04
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2009
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M. Cacciari, G. P. Salam, and G. Soyez, Eur. Phys. J. C72
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2014
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2014
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2015
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Then

T. Heimel, G. Kasieczka, T. Plehn, and J. M. Thompson, SciPost Phys. 6
Original
2019
Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih, “R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge,” (2019)
2019
Later among the works it cites.
T. Aarrestad et al. , (2021), arXiv:2105.14027 [hep-ph]
Original
2021
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G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, and D. Shih, (2021), arXiv:2112.03769 [hep-ph]
Original
2021
Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih (2021) arXiv:2107.02821 [stat.ML]
Original
2021
Later among the works it cites.

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2022

Resonant anomaly detection without background sculpting

Hallin, Anna, Kasieczka, Gregor, Quadfasel, Tobias et al.

Understand

We introduce a new technique named Latent CATHODE (LaCATHODE) for performing "enhanced bump hunts", a type of resonant anomaly search that combines conventional one-dimensional bump hunts with a model-agnostic anomaly score in an auxiliary feature space where potential signals could also be localized.

The main advantage of LaCATHODE over existing methods is that it provides an anomaly score that is well behaved when evaluating it beyond the signal region, which is essential to prevent the sculpting of background distributions in the bump hunt.
LaCATHODE accomplishes this by constructing the anomaly score directly in the latent space learned by a conditional normalizing flow trained on sideband regions.
We demonstrate the superior stability and comparable performance of LaCATHODE for enhanced bump hunting in an illustrative toy example as well as on the LHC Olympics R&D dataset.

Built on

J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D99
Original
1902
Earlier work this paper cites.
J. Neyman and E. S. Pearson, Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 231
1933
Earlier work this paper cites.
B. Nachman and D. Shih, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman, and D. Shih, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
ATLAS Collaboration, (2020), 10.1103/PhysRevLett.125.131801 , arXiv:2005.02983 [hep-ex]
Original
2005
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, JHEP 05
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Phys. Lett. B641
2006
Earlier work this paper cites.
T. Sjöstrand, S. Mrenna, and P. Z. Skands, Comput. Phys. Commun. 178
Original
2008
Earlier work this paper cites.
M. Cacciari, G. P. Salam, and G. Soyez, JHEP 04
Original
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.

Similar

M. Cacciari, G. P. Salam, and G. Soyez, Eur. Phys. J. C72
Original
2012
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” (2014), arXiv:1412.6980 [cs.LG]
Original
2014
Cited alongside, same era.
J. de Favereau, C. Delaere, P. Demin, A. Giammanco, V. Lemaitre, A. Mertens, and M. Selvaggi (DELPHES 3), JHEP 02
Original
2014
Cited alongside, same era.
M. Selvaggi, Proceedings, 15th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2013): Beijing, China, May 16-21, 2013 , J. Phys. Conf. Ser. 523
2014
Cited alongside, same era.
F. Chollet et al. , “Keras,” (2015)
2015
Cited alongside, same era.

Then

T. Heimel, G. Kasieczka, T. Plehn, and J. M. Thompson, SciPost Phys. 6
Original
2019
Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih, “R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge,” (2019)
2019
Later among the works it cites.
T. Aarrestad et al. , (2021), arXiv:2105.14027 [hep-ph]
Original
2021
Later among the works it cites.
G. Karagiorgi, G. Kasieczka, S. Kravitz, B. Nachman, and D. Shih, (2021), arXiv:2112.03769 [hep-ph]
Original
2021
Later among the works it cites.
G. Kasieczka, B. Nachman, and D. Shih (2021) arXiv:2107.02821 [stat.ML]
Original
2021
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-scale machine learning on heterogeneous systems,” (2015), software available from tensorflow.org

2015

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A. Mertens, Proceedings, 16th International workshop on Advanced Computing and Analysis Techniques in physics (ACAT 14): Prague, Czech Republic, September 1-5, 2014 , J. Phys. Conf. Ser. 608

2015

Cited alongside, same era.

M. Farina, Y. Nakai, and D. Shih, (2018), 10.1103/PhysRevD.101.075021 , arXiv:1808.08992 [hep-ph]

Original

2018

Cited alongside, same era.

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

Original

2018

Cited alongside, same era.

G. Kasieczka et al. , (2021a), arXiv:2101.08320 [hep-ph]

Original