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Toward the end-to-end optimization of the SWGO array layout
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. MODE Collaboration, Spain; Istituto Nazionale di Fisica Nucleare - Sezione di Padova, via Marzolo 8, Padova, 35131, Italy; Universal Scientific Education and Research Network (USERN), Italy.ORCID iD: 0000-0002-1659-8727
MODE Collaboration, Spain; University of Kaiserslautern-Landau (RPTU), Germany.
Istituto Nazionale di Fisica Nucleare - Sezione di Padova, via Marzolo 8, Padova, 35131, Italy.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. Istituto Nazionale di Fisica Nucleare - Sezione di Padova, via Marzolo 8, Padova, 35131, Italy; Universal Scientific Education and Research Network (USERN), Italy; Dipartimento di Fisica e Astronomia G. Galilei, University of Padova, via Marzolo 8, Padova, 35131, Italy.ORCID iD: 0009-0001-3665-9507
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2025 (English)In: Nuclear Physics B, ISSN 0550-3213, E-ISSN 1873-1562, Vol. 1017, article id 116934Article in journal (Refereed) Published
Abstract [en]

In this document we consider the problem of finding the optimal layout for the array of water Cherenkov detectors proposed by the SWGO collaboration to study very-high-energy gamma rays in the southern hemisphere. We develop a continuous model of the secondary particles produced by atmospheric showers initiated by high-energy gamma rays and protons, and build an optimization pipeline capable of identifying the most promising configuration of the detector elements. The pipeline employs stochastic gradient descent to maximize a utility function aligned with the scientific goals of the experiment. We demonstrate how the software is capable of finding the global maximum in the high-dimensional parameter space, and discuss its performance and limitations.

Place, publisher, year, edition, pages
Elsevier B.V. , 2025. Vol. 1017, article id 116934
Keywords [en]
Gamma rays, Optimization, Cosmic rays, Cherenkov detectors
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-112834DOI: 10.1016/j.nuclphysb.2025.116934Scopus ID: 2-s2.0-105004307675OAI: oai:DiVA.org:ltu-112834DiVA, id: diva2:1976047
Note

Validerad;2025;Nivå 2;2025-06-24 (u5);

Full text license: CC BY 4.0;

Funder: Alexander-von-Humboldt-Stiftung; German National High Performance Computing (NHR) association;

Available from: 2025-06-24 Created: 2025-06-24 Last updated: 2025-06-24Bibliographically approved

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Dorigo, TommasoAwais, Muhammad

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