Gutenberg Open Science: Learning to rank Higgs boson candidates

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-8438

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DC Field	Value	Language
dc.contributor.author	Köppel, Marius	-
dc.contributor.author	Segner, Alexander	-
dc.contributor.author	Wagener, Martin	-
dc.contributor.author	Pensel, Lukas	-
dc.contributor.author	Karwath, Andreas	-
dc.contributor.author	Schmitt, Christian	-
dc.contributor.author	Kramer, Stefan	-
dc.date.accessioned	2022-11-30T09:58:46Z	-
dc.date.available	2022-11-30T09:58:46Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/8454	-
dc.description.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.	en_GB
dc.description.sponsorship	Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	530 Physik	de_DE
dc.subject.ddc	530 Physics	en_GB
dc.title	Learning to rank Higgs boson candidates	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-8438	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Scientific reports	de
jgu.journal.volume	12	de
jgu.pages.alternative	13094	de
jgu.publisher.year	2022	-
jgu.publisher.name	Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	2045-2322	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	530	de
jgu.publisher.doi	10.1038/s41598-022-10383-w	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Naturwissenschaften	de
Appears in collections:	DFG-491381577-G

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