Academic Journal
Determination of the HERA coherent diffractive J∕ψ production cross-section via artificial neural network.
| Title: | Determination of the HERA coherent diffractive J∕ψ production cross-section via artificial neural network. |
|---|---|
| Authors: | Baihaqi, Taufiq I., Setyadi, Chalis, Akbar, Zulkaida, Hutauruk, Parada T. P., Adam, Apriadi Salim |
| Source: | Modern Physics Letters A; 10/10/2026, Vol. 41 Issue 30/31, p1-18, 18p |
| Subject Terms: | Artificial neural networks, Particle accelerators, Physical constants |
| Abstract: | An exclusive coherent diffractive J ∕ ψ production dataset from HERA, covering a large kinematic range in the photon virtuality Q 2 , the squared momentum transfer t, and the photon–proton center-of-mass energy W, has been analyzed using various theoretical models with different approaches. In common model analyses, the inherent assumptions and limited kinematic applicability somewhat restrict the predictive power of the models, resulting in model-dependent prediction results. In this paper, we present our model-independent approach for the same reaction process and dataset, utilizing an Artificial Neural Network (ANN) technique. The prediction of the best ANN model for the HERA differential cross-section dataset over a range of W, Q 2 , and t is obtained. We then extend the ANN model by combining the HERA and LHC data at various values of W to predict the total photoproduction cross-section and demonstrate how to extract the exponential slope b. We find that the exponential slope b strongly depends on Q 2 and W. [ABSTRACT FROM AUTHOR] |
| Copyright of Modern Physics Letters A is the property of World Scientific Publishing Company 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: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 196446576 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.43054199219 |
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| Items | – Name: Title Label: Title Group: Ti Data: Determination of the HERA coherent diffractive J∕ψ production cross-section via artificial neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Baihaqi%2C+Taufiq+I%2E%22">Baihaqi, Taufiq I.</searchLink><br /><searchLink fieldCode="AR" term="%22Setyadi%2C+Chalis%22">Setyadi, Chalis</searchLink><br /><searchLink fieldCode="AR" term="%22Akbar%2C+Zulkaida%22">Akbar, Zulkaida</searchLink><br /><searchLink fieldCode="AR" term="%22Hutauruk%2C+Parada+T%2E+P%2E%22">Hutauruk, Parada T. P.</searchLink><br /><searchLink fieldCode="AR" term="%22Adam%2C+Apriadi+Salim%22">Adam, Apriadi Salim</searchLink> – Name: TitleSource Label: Source Group: Src Data: Modern Physics Letters A; 10/10/2026, Vol. 41 Issue 30/31, p1-18, 18p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+accelerators%22">Particle accelerators</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+constants%22">Physical constants</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: An exclusive coherent diffractive J ∕ ψ production dataset from HERA, covering a large kinematic range in the photon virtuality Q 2 , the squared momentum transfer t, and the photon–proton center-of-mass energy W, has been analyzed using various theoretical models with different approaches. In common model analyses, the inherent assumptions and limited kinematic applicability somewhat restrict the predictive power of the models, resulting in model-dependent prediction results. In this paper, we present our model-independent approach for the same reaction process and dataset, utilizing an Artificial Neural Network (ANN) technique. The prediction of the best ANN model for the HERA differential cross-section dataset over a range of W, Q 2 , and t is obtained. We then extend the ANN model by combining the HERA and LHC data at various values of W to predict the total photoproduction cross-section and demonstrate how to extract the exponential slope b. We find that the exponential slope b strongly depends on Q 2 and W. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Modern Physics Letters A is the property of World Scientific Publishing Company 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S021773232642006X Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 1 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Particle accelerators Type: general – SubjectFull: Physical constants Type: general Titles: – TitleFull: Determination of the HERA coherent diffractive J∕ψ production cross-section via artificial neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Baihaqi, Taufiq I. – PersonEntity: Name: NameFull: Setyadi, Chalis – PersonEntity: Name: NameFull: Akbar, Zulkaida – PersonEntity: Name: NameFull: Hutauruk, Parada T. P. – PersonEntity: Name: NameFull: Adam, Apriadi Salim IsPartOfRelationships: – BibEntity: Dates: – D: 10 M: 10 Text: 10/10/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02177323 Numbering: – Type: volume Value: 41 – Type: issue Value: 30/31 Titles: – TitleFull: Modern Physics Letters A Type: main |
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