Physics instrument design with reinforcement learning
We present a case for the use of reinforcement learning (RL) for the design of physics instruments as an alternative to gradient-based instrument-optimization methods. Its applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is bo...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Machine Learning: Science and Technology |
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| Online Access: | https://doi.org/10.1088/2632-2153/adf7ff |
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| _version_ | 1849764918475096064 |
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| author | Shah Rukh Qasim Patrick Owen Nicola Serra |
| author_facet | Shah Rukh Qasim Patrick Owen Nicola Serra |
| author_sort | Shah Rukh Qasim |
| collection | DOAJ |
| description | We present a case for the use of reinforcement learning (RL) for the design of physics instruments as an alternative to gradient-based instrument-optimization methods. Its applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well as longitudinal placement of trackers in a spectrometer. In both of the experiments, the RL agent found non-trivial designs which outperformed the baselines. Based on these experiments, we propose an alternative approach that offers unique advantages over differentiable programming and surrogate-based differentiable design optimization methods. First, RL algorithms possess inherent exploratory capabilities, which help mitigate the risk of convergence to local optima. Second, this approach eliminates the necessity of constraining the design to a predefined detector model with fixed parameters. Instead, it allows for the flexible placement of a variable number of detector components and facilitates discrete decision-making. We then discuss the road map of how this idea can be extended into designing very complex instruments. The presented study sets the stage for a novel framework in physics instrument design, offering a scalable and efficient framework that can be pivotal for future projects such as the future circular collider, where highly optimized detectors are essential for exploring physics at unprecedented energy scales. |
| format | Article |
| id | doaj-art-ed72efaca0d8406abf727573c0c66d81 |
| institution | DOAJ |
| issn | 2632-2153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Machine Learning: Science and Technology |
| spelling | doaj-art-ed72efaca0d8406abf727573c0c66d812025-08-20T03:05:01ZengIOP PublishingMachine Learning: Science and Technology2632-21532025-01-016303503310.1088/2632-2153/adf7ffPhysics instrument design with reinforcement learningShah Rukh Qasim0https://orcid.org/0000-0003-4264-9724Patrick Owen1https://orcid.org/0000-0002-4161-9147Nicola Serra2https://orcid.org/0000-0002-5033-0580University of Zurich , Physics Institute, Zurich, SwitzerlandUniversity of Zurich , Physics Institute, Zurich, SwitzerlandUniversity of Zurich , Physics Institute, Zurich, SwitzerlandWe present a case for the use of reinforcement learning (RL) for the design of physics instruments as an alternative to gradient-based instrument-optimization methods. Its applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well as longitudinal placement of trackers in a spectrometer. In both of the experiments, the RL agent found non-trivial designs which outperformed the baselines. Based on these experiments, we propose an alternative approach that offers unique advantages over differentiable programming and surrogate-based differentiable design optimization methods. First, RL algorithms possess inherent exploratory capabilities, which help mitigate the risk of convergence to local optima. Second, this approach eliminates the necessity of constraining the design to a predefined detector model with fixed parameters. Instead, it allows for the flexible placement of a variable number of detector components and facilitates discrete decision-making. We then discuss the road map of how this idea can be extended into designing very complex instruments. The presented study sets the stage for a novel framework in physics instrument design, offering a scalable and efficient framework that can be pivotal for future projects such as the future circular collider, where highly optimized detectors are essential for exploring physics at unprecedented energy scales.https://doi.org/10.1088/2632-2153/adf7ffreinforcement learningdetector optimizationinstrument designmachine learningmachine learning for high energy physicsRL for instrument design |
| spellingShingle | Shah Rukh Qasim Patrick Owen Nicola Serra Physics instrument design with reinforcement learning Machine Learning: Science and Technology reinforcement learning detector optimization instrument design machine learning machine learning for high energy physics RL for instrument design |
| title | Physics instrument design with reinforcement learning |
| title_full | Physics instrument design with reinforcement learning |
| title_fullStr | Physics instrument design with reinforcement learning |
| title_full_unstemmed | Physics instrument design with reinforcement learning |
| title_short | Physics instrument design with reinforcement learning |
| title_sort | physics instrument design with reinforcement learning |
| topic | reinforcement learning detector optimization instrument design machine learning machine learning for high energy physics RL for instrument design |
| url | https://doi.org/10.1088/2632-2153/adf7ff |
| work_keys_str_mv | AT shahrukhqasim physicsinstrumentdesignwithreinforcementlearning AT patrickowen physicsinstrumentdesignwithreinforcementlearning AT nicolaserra physicsinstrumentdesignwithreinforcementlearning |