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Authors: Köppel, Marius
Segner, Alexander
Wagener, Martin
Pensel, Lukas
Karwath, Andreas
Schmitt, Christian
Kramer, Stefan
Title: Learning to rank Higgs boson candidates
Online publication date: 30-Nov-2022
Year of first publication: 2022
Language: english
Abstract: In the extensive search for new physics, the precise measurement of the Higgs boson continues to play an important role. To this end, machine learning techniques have been recently applied to processes like the Higgs production via vector-boson fusion. In this paper, we propose to use algorithms for learning to rank, i.e., to rank events into a sorting order, first signal, then background, instead of algorithms for the classification into two classes, for this task. The fact that training is then performed on pairwise comparisons of signal and background events can effectively increase the amount of training data due to the quadratic number of possible combinations. This makes it robust to unbalanced data set scenarios and can improve the overall performance compared to pointwise models like the state-of-the-art boosted decision tree approach. In this work we compare our pairwise neural network algorithm, which is a combination of a convolutional neural network and the DirectRanker, with convolutional neural networks, multilayer perceptrons or boosted decision trees, which are commonly used algorithms in multiple Higgs production channels. Furthermore, we use so-called transfer learning techniques to improve overall performance on different data types.
DDC: 530 Physik
530 Physics
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 08 Physik, Mathematik u. Informatik
Place: Mainz
Version: Published version
Publication type: Zeitschriftenaufsatz
Document type specification: Scientific article
License: CC BY
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Journal: Scientific reports
Pages or article number: 13094
Publisher: Springer Nature
Publisher place: London
Issue date: 2022
ISSN: 2045-2322
Publisher DOI: 10.1038/s41598-022-10383-w
Appears in collections:DFG-491381577-G

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