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J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D 99
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1902
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2022

Null Hypothesis Test for Anomaly Detection

Kamenik, Jernej F., Szewc, Manuel

Understand

We extend the use of Classification Without Labels for anomaly detection with a hypothesis test designed to exclude the background-only hypothesis.

By testing for statistical independence of the two discriminating dataset regions, we are able to exclude the background-only hypothesis without relying on fixed anomaly score cuts or extrapolations of background estimates between regions.
The method relies on the assumption of conditional independence of anomaly score features and dataset regions, which can be ensured using existing decorrelation techniques.
As a benchmark example, we consider the LHC Olympics dataset where we show that mutual information represents a suitable test for statistical independence and our method exhibits excellent and robust performance at different signal fractions even in presence of realistic feature correlations.

Built on

J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. D 99
Original
1902
Earlier work this paper cites.
B. Nachman, SciPost Phys. 8
Original
1909
Earlier work this paper cites.
E. Alvarez, F. Lamagna, and M. Szewc, JHEP 01
Original
1911
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
W. Hoeffding, The Annals of Mathematical Statistics 19
1948
Earlier work this paper cites.
B. Goebel, Z. Dawy, J. Hagenauer, and J. Mueller (2005) pp. 1102 – 1106 Vol. 2
2005
Earlier work this paper cites.
G. J. Székely, M. L. Rizzo, and N. K. Bakirov, The Annals of Statistics 35
2007
Earlier work this paper cites.
K. Benkendorfer, L. L. Pottier, and B. Nachman, Phys. Rev. D 104
Original
2009
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, JHEP 03
Original
2011
Earlier work this paper cites.

Similar

J. Thaler and K. Van Tilburg, JHEP 02
Original
2012
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” (2014)
2014
Cited alongside, same era.
C. M. Bishop, Pattern recognition and machine learning , Information science and statistics (Springer, New York, NY, 2006) softcover published in 2016
2016
Cited alongside, same era.
E. M. Metodiev, B. Nachman, and J. Thaler, JHEP 10
Original
2017
Cited alongside, same era.
J. H. Collins, K. Howe, and B. Nachman, Phys. Rev. Lett. 121
Original
2018
Cited alongside, same era.

Then

G. Kasieczka et al. , (2021), arXiv:2101.08320 [hep-ph]
Original
2021
Later among the works it cites.
P. Shanahan et al. , (2022), arXiv:2209.07559 [physics.comp-ph]
Original
2022
Closest in time.
S. E. Park, P. Harris, and B. Ostdiek, (2022), arXiv:2208.05484 [hep-ph]
Original
2022
Closest in time.
G. Kasieczka, R. Mastandrea, V. Mikuni, B. Nachman, M. Pettee, and D. Shih, (2022), arXiv:2209.06225 [hep-ph]
Original
2022
Closest in time.
R. T. d’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Eur. Phys. J. C 82
Original
2022

Beyond the bibliography

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R. T. D’Agnolo and A. Wulzer, Phys. Rev. D 99

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P. Chakravarti, M. Kuusela, J. Lei, and L. Wasserman, (2021), arXiv:2102.07679 [stat.AP]

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Cited alongside, same era.

HEP ML Community, “A Living Review of Machine Learning for Particle Physics,”

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“Null Hypothesis Test for Anomaly Detection,” https://github.com/ManuelSzewc/Null_Hypothesis_Test_for_Anomaly_Detection

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M. Letizia, G. Losapio, M. Rando, G. Grosso, A. Wulzer, M. Pierini, M. Zanetti, and L. Rosasco, Eur. Phys. J. C 82