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Judith M. Katzy

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Adversarial domain adaptation to reduce sample bias of a high energy physics classifier

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May 01, 2020
Jose M. Clavijo, Paul Glaysher, Judith M. Katzy

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We apply adversarial domain adaptation to reduce sample bias in a classification machine learning algorithm. We add a gradient reversal layer to a neural network to simultaneously classify signal versus background events, while minimising the difference of the classifier response to a background sample using an alternative MC model. We show this on the example of simulated events at the LHC with $t\bar{t}H$ signal versus $t\bar{t}b\bar{b}$ background classification.

* 15 pages, 8 figures, to be submitted to JINST 
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Iterative subtraction method for Feature Ranking

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Jun 13, 2019
Paul Glaysher, Judith M. Katzy, Sitong An

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Training features used to analyse physical processes are often highly correlated and determining which ones are most important for the classification is a non-trivial tasks. For the use case of a search for a top-quark pair produced in association with a Higgs boson decaying to bottom-quarks at the LHC, we compare feature ranking methods for a classification BDT. Ranking methods, such as the BDT Selection Frequency commonly used in High Energy Physics and the Permutational Performance, are compared with the computationally expense Iterative Addition and Iterative Removal procedures, while the latter was found to be the most performant.

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