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J. H. Collins, K. Howe and B. Nachman, Extending the search for new resonances with machine learning , Phys. Rev. D 99
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders , [ 1903.02032 ]
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches , JHEP 10
Original
1905
Earlier work this paper cites.
J. Brehmer, F. Kling, I. Espejo and K. Cranmer, MadMiner: Machine learning-based inference for particle physics , Comput. Softw. Big Sci. 4
Original
1907
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, Learning multivariate new physics , Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the Problem of the Most Efficient Tests of Statistical Hypotheses , Phil. Trans. Roy. Soc. Lond. A 231
1933
Earlier work this paper cites.
S. S. Wilks, The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , Annals Math. Statist. 9
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. 54
1943
Earlier work this paper cites.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
A. Andreassen, B. Nachman and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, Tag N’ Train: a technique to train improved classifiers on unlabeled data , JHEP 01
Original
2002
Earlier work this paper cites.
K. T. Matchev, P. Shyamsundar and J. Smolinsky, A quantum algorithm for model independent searches for new physics , [ 2003.02181 ]
Original
2003
Earlier work this paper cites.
O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark , Eur. Phys. J. Plus 136
Original
2005
Earlier work this paper cites.
T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual , JHEP 05
2006
Earlier work this paper cites.
S. Chen, A. Glioti, G. Panico and A. Wulzer, Parametrized classifiers for optimal EFT sensitivity , JHEP 05
Original
2007
Earlier work this paper cites.

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2021

Learning New Physics from an Imperfect Machine

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

Understand

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks.

Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics.
After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem.
We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.