Please use this identifier to cite or link to this item:
http://doi.org/10.25358/openscience-8438
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 |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-8438 |
Version: | Published version |
Publication type: | Zeitschriftenaufsatz |
Document type specification: | Scientific article |
License: | CC BY |
Information on rights of use: | https://creativecommons.org/licenses/by/4.0/ |
Journal: | Scientific reports 12 |
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 |
Files in This Item:
File | Description | Size | Format | ||
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![]() | learning_to_rank_higgs_boson_-20221129142953611.pdf | 1.17 MB | Adobe PDF | View/Open |