Autoren:	Köppel, Marius Segner, Alexander Wagener, Martin Pensel, Lukas Karwath, Andreas Schmitt, Christian Kramer, Stefan
Titel:	Learning to rank Higgs boson candidates
Online-Publikationsdatum:	30-Nov-2022
Erscheinungsdatum:	2022
Sprache des Dokuments:	Englisch
Zusammenfassung/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-Sachgruppe:	530 Physik 530 Physics
Veröffentlichende Institution:	Johannes Gutenberg-Universität Mainz
Organisationseinheit:	FB 08 Physik, Mathematik u. Informatik
Veröffentlichungsort:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-8438
Version:	Published version
Publikationstyp:	Zeitschriftenaufsatz
Weitere Angaben zur Dokumentart:	Scientific article
Nutzungsrechte:	CC BY
Informationen zu den Nutzungsrechten:	https://creativecommons.org/licenses/by/4.0/
Zeitschrift:	Scientific reports 12
Seitenzahl oder Artikelnummer:	13094
Verlag:	Springer Nature
Verlagsort:	London
Erscheinungsdatum:	2022
ISSN:	2045-2322
DOI der Originalveröffentlichung:	10.1038/s41598-022-10383-w
Enthalten in den Sammlungen:	DFG-491381577-G