Mass-unspecific classifiers for mass-dependent searches.
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| Title: | Mass-unspecific classifiers for mass-dependent searches. |
|---|---|
| Authors: | Aguilar-Saavedra, J. A.1 (AUTHOR) ja.a.s@csic.es, Rodríguez-Benítez, S.1 (AUTHOR) |
| Source: | European Physical Journal C -- Particles & Fields. Feb2026, Vol. 86 Issue 2, p1-7. 7p. |
| Subjects: | Particle physics, Boosting algorithms, Statistical correlation, Pattern recognition systems, Artificial neural networks |
| Abstract: | Searches for new particles often span a wide range of mass scales, where the shape of potential signals and the SM background varies significantly. We make use of a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale, and is trained on a sample that is balanced across the entire mass range. The classifiers, either a neural network or a boosted decision tree, produce a continuous output across the full mass range and, at a given mass, achieve nearly the same performance as a classifier specifically trained for that mass. The performance of the classifiers is better than the one obtained with parameterised neural networks and similar methods. [ABSTRACT FROM AUTHOR] |
| Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192428949 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mass-unspecific classifiers for mass-dependent searches. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aguilar-Saavedra%2C+J%2E+A%2E%22">Aguilar-Saavedra, J. A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ja.a.s@csic.es</i><br /><searchLink fieldCode="AR" term="%22Rodríguez-Benítez%2C+S%2E%22">Rodríguez-Benítez, S.</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Physical+Journal+C+--+Particles+%26+Fields%22">European Physical Journal C -- Particles & Fields</searchLink>. Feb2026, Vol. 86 Issue 2, p1-7. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Particle+physics%22">Particle physics</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+correlation%22">Statistical correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Searches for new particles often span a wide range of mass scales, where the shape of potential signals and the SM background varies significantly. We make use of a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale, and is trained on a sample that is balanced across the entire mass range. The classifiers, either a neural network or a boosted decision tree, produce a continuous output across the full mass range and, at a given mass, achieve nearly the same performance as a classifier specifically trained for that mass. The performance of the classifiers is better than the one obtained with parameterised neural networks and similar methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Physical Journal C -- Particles & Fields is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192428949 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1140/epjc/s10052-026-15314-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 1 Subjects: – SubjectFull: Particle physics Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Statistical correlation Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: Mass-unspecific classifiers for mass-dependent searches. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aguilar-Saavedra, J. A. – PersonEntity: Name: NameFull: Rodríguez-Benítez, S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 14346044 Numbering: – Type: volume Value: 86 – Type: issue Value: 2 Titles: – TitleFull: European Physical Journal C -- Particles & Fields Type: main |
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