Learning to rank Higgs boson candidates
- Publication type:
- Journal article
- Metadata:
-
- Autoren
- Marius Koeppel
- Alexander Segner
- Martin Wagener
- Lukas Pensel
- Andreas Karwath
- Christian Schmitt
- Stefan Kramer
- Autoren-URL
- https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000834790800037&DestLinkType=FullRecord&DestApp=WOS_CPL
- DOI
- 10.1038/s41598-022-10383-w
- Externe Identifier
- Clarivate Analytics Document Solution ID: 3L5HC
- PubMed Identifier: 35908043
- ISSN
- 2045-2322
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- SCIENTIFIC REPORTS
- Artikelnummer
- ARTN 13094
- Datum der Veröffentlichung
- 2022
- Status
- Published
- Titel
- Learning to rank Higgs boson candidates
- Sub types
- Article
- Ausgabe der Zeitschrift
- 12
Data source: Web of Science (Lite)
- Other metadata sources:
-
- Abstract
- <jats:title>Abstract</jats:title><jats:p>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.</jats:p>
- Autoren
- Marius Köppel
- Alexander Segner
- Martin Wagener
- Lukas Pensel
- Andreas Karwath
- Christian Schmitt
- Stefan Kramer
- DOI
- 10.1038/s41598-022-10383-w
- eISSN
- 2045-2322
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Scientific Reports
- Sprache
- en
- Artikelnummer
- 13094
- Online publication date
- 2022
- Status
- Published online
- Herausgeber
- Springer Science and Business Media LLC
- Herausgeber URL
- http://dx.doi.org/10.1038/s41598-022-10383-w
- Datum der Datenerfassung
- 2022
- Titel
- Learning to rank Higgs boson candidates
- Ausgabe der Zeitschrift
- 12
Data source: Crossref
- 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.
- Addresses
- Johannes Gutenberg University, Mainz, Germany. mkoeppel@uni-mainz.de.
- Autoren
- Marius Köppel
- Alexander Segner
- Martin Wagener
- Lukas Pensel
- Andreas Karwath
- Christian Schmitt
- Stefan Kramer
- DOI
- 10.1038/s41598-022-10383-w
- eISSN
- 2045-2322
- Externe Identifier
- PubMed Identifier: 35908043
- PubMed Central ID: PMC9338962
- Funding acknowledgements
- Medical Research Charities Group: MR/S003991/1
- Johannes Gutenberg-Universität Mainz:
- Medical Research Council: MR/S003991/1
- Open access
- true
- ISSN
- 2045-2322
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Scientific reports
- Sprache
- eng
- Medium
- Electronic
- Online publication date
- 2022
- Open access status
- Open Access
- Paginierung
- 13094
- Datum der Veröffentlichung
- 2022
- Status
- Published
- Publisher licence
- CC BY
- Datum der Datenerfassung
- 2022
- Titel
- Learning to rank Higgs boson candidates.
- Sub types
- research-article
- Journal Article
- Ausgabe der Zeitschrift
- 12
Files
https://www.nature.com/articles/s41598-022-10383-w.pdf https://europepmc.org/articles/PMC9338962?pdf=render
Data source: Europe PubMed Central
- 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.
- Date of acceptance
- 2022
- Autoren
- Marius Köppel
- Alexander Segner
- Martin Wagener
- Lukas Pensel
- Andreas Karwath
- Christian Schmitt
- Stefan Kramer
- Autoren-URL
- https://www.ncbi.nlm.nih.gov/pubmed/35908043
- DOI
- 10.1038/s41598-022-10383-w
- eISSN
- 2045-2322
- Externe Identifier
- PubMed Central ID: PMC9338962
- Funding acknowledgements
- Medical Research Council: MR/S003991/1
- Medical Research Charities Group: MR/S003991/1
- Ausgabe der Veröffentlichung
- 1
- Zeitschrift
- Sci Rep
- Sprache
- eng
- Country
- England
- Paginierung
- 13094
- PII
- 10.1038/s41598-022-10383-w
- Datum der Veröffentlichung
- 2022
- Status
- Published online
- Titel
- Learning to rank Higgs boson candidates.
- Sub types
- Journal Article
- Ausgabe der Zeitschrift
- 12
Data source: PubMed
- Author's licence
- CC-BY
- Autoren
- Marius Köppel
- Alexander Segner
- Martin Wagener
- Lukas Pensel
- Andreas Karwath
- Christian Schmitt
- Stefan Kramer
- Hosting institution
- Universitätsbibliothek Mainz
- Sammlungen
- DFG-491381577-G
- Resource version
- Published version
- DOI
- 10.1038/s41598-022-10383-w
- Funding acknowledgements
- Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 491381577
- File(s) embargoed
- false
- Open access
- true
- ISSN
- 2045-2322
- Zeitschrift
- Scientific reports
- Schlüsselwörter
- 530 Physik
- 530 Physics
- Sprache
- eng
- Open access status
- Open Access
- Paginierung
- 13094
- Datum der Veröffentlichung
- 2022
- Public URL
- https://openscience.ub.uni-mainz.de/handle/20.500.12030/8454
- Herausgeber
- Springer Nature
- Datum der Datenerfassung
- 2022
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2022
- Zugang
- Public
- Titel
- Learning to rank Higgs boson candidates
- Ausgabe der Zeitschrift
- 12
Files
learning_to_rank_higgs_boson_-20221129142953611.pdf
Data source: OPENSCIENCE.UB
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- Property of