On the utility function of experiments in fundamental science
The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to exte...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Physics Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666032625000201 |
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| author | Tommaso Dorigo Michele Doro Max Aehle Muhammad Awais Nicolas R. Gauger Rafael Izbicki Jan Kieseler Ann B. Lee Luca Masserano Federico Nardi Alexander Shen Luis Recabarren Vergara |
| author_facet | Tommaso Dorigo Michele Doro Max Aehle Muhammad Awais Nicolas R. Gauger Rafael Izbicki Jan Kieseler Ann B. Lee Luca Masserano Federico Nardi Alexander Shen Luis Recabarren Vergara |
| author_sort | Tommaso Dorigo |
| collection | DOAJ |
| description | The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued; this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals.In this work we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front. For that purpose, we consider two use cases in particle physics research and one in astro-particle physics; in the latter case we show, using a recently developed optimization software, how the precise definition of a multi-target utility function may enable a significant increase of its value above that offered by human-designed detector layouts. |
| format | Article |
| id | doaj-art-67863fe163c94196aa8ca74f15fa9c7e |
| institution | Kabale University |
| issn | 2666-0326 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics Open |
| spelling | doaj-art-67863fe163c94196aa8ca74f15fa9c7e2025-08-20T03:49:45ZengElsevierPhysics Open2666-03262025-05-012310027010.1016/j.physo.2025.100270On the utility function of experiments in fundamental scienceTommaso Dorigo0Michele Doro1Max Aehle2Muhammad Awais3Nicolas R. Gauger4Rafael Izbicki5Jan Kieseler6Ann B. Lee7Luca Masserano8Federico Nardi9Alexander Shen10Luis Recabarren Vergara11Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden; Istituto Nazionale di Fisica Nucleare - Sezione di Padova, 35131 Padova, Italy; Universal Scientific Education and Research Network, Italy; MODE Collaboration, Italy; Corresponding author at: Istituto Nazionale di Fisica Nucleare - Sezione di Padova, 35131 Padova, Italy.Università di Padova, Dipartimento di Fisica e Astronomia “G.Galilei”, via F. Marzolo 8, 35131 Padova, Italy; Istituto Nazionale di Fisica Nucleare - Sezione di Padova, 35131 Padova, ItalyChair for Scientific Computing, University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Straße, 67663 Kaiserslautern, GermanyDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden; Istituto Nazionale di Fisica Nucleare - Sezione di Padova, 35131 Padova, Italy; Università di Padova, Dipartimento di Fisica e Astronomia “G.Galilei”, via F. Marzolo 8, 35131 Padova, ItalyChair for Scientific Computing, University of Kaiserslautern-Landau (RPTU), Gottlieb-Daimler-Straße, 67663 Kaiserslautern, GermanyDepartment of Statistics, Federal University of São Carlos, São Carlos, BrazilKarlsruhe Institute for Technology, Kaiserstraße 12, 76131 Karlsruhe, GermanyDepartment of Statistics & Data Science, Department of Machine Learning, Carnegie Mellon University, Pittsburgh, USADepartment of Statistics & Data Science, Department of Machine Learning, Carnegie Mellon University, Pittsburgh, USAUniversità di Padova, Dipartimento di Fisica e Astronomia “G.Galilei”, via F. Marzolo 8, 35131 Padova, Italy; Laboratoire de Physique de Clermont Auvergne, 4 Avenue Blaise Pascal, 63170 Aubière, FranceDepartment of Statistics & Data Science, Department of Machine Learning, Carnegie Mellon University, Pittsburgh, USACentro di Ateneo di Studi e Attività Spaziali “Giuseppe Colombo”, Via Venezia 15, I-35131 Padova, Italy; Istituto Nazionale di Fisica Nucleare - Sezione di Padova, 35131 Padova, Italy; Università di Padova, Dipartimento di Fisica e Astronomia “G.Galilei”, via F. Marzolo 8, 35131 Padova, ItalyThe majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued; this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals.In this work we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front. For that purpose, we consider two use cases in particle physics research and one in astro-particle physics; in the latter case we show, using a recently developed optimization software, how the precise definition of a multi-target utility function may enable a significant increase of its value above that offered by human-designed detector layouts.http://www.sciencedirect.com/science/article/pii/S2666032625000201OptimizationExperiment designParticle physicsParticle detectorsAstrophysics |
| spellingShingle | Tommaso Dorigo Michele Doro Max Aehle Muhammad Awais Nicolas R. Gauger Rafael Izbicki Jan Kieseler Ann B. Lee Luca Masserano Federico Nardi Alexander Shen Luis Recabarren Vergara On the utility function of experiments in fundamental science Physics Open Optimization Experiment design Particle physics Particle detectors Astrophysics |
| title | On the utility function of experiments in fundamental science |
| title_full | On the utility function of experiments in fundamental science |
| title_fullStr | On the utility function of experiments in fundamental science |
| title_full_unstemmed | On the utility function of experiments in fundamental science |
| title_short | On the utility function of experiments in fundamental science |
| title_sort | on the utility function of experiments in fundamental science |
| topic | Optimization Experiment design Particle physics Particle detectors Astrophysics |
| url | http://www.sciencedirect.com/science/article/pii/S2666032625000201 |
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