Automating Sensor Characterization with Bayesian Optimization
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866917207009656832 |
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| author | Cuevas-Zepeda, J. Chavez, C. Estrada, J. Noonan, J. Nord, B. D. Saffold, N. Sofo-Haro, M. Castro, R. Spinola e Trivedi, S. |
| author_facet | Cuevas-Zepeda, J. Chavez, C. Estrada, J. Noonan, J. Nord, B. D. Saffold, N. Sofo-Haro, M. Castro, R. Spinola e Trivedi, S. |
| contents | The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor characterization that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating Sensor Characterization with Bayesian Optimization Cuevas-Zepeda, J. Chavez, C. Estrada, J. Noonan, J. Nord, B. D. Saffold, N. Sofo-Haro, M. Castro, R. Spinola e Trivedi, S. Instrumentation and Detectors Instrumentation and Methods for Astrophysics Machine Learning The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor characterization that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert. |
| title | Automating Sensor Characterization with Bayesian Optimization |
| topic | Instrumentation and Detectors Instrumentation and Methods for Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2509.21661 |