Automating Sensor Characterization with Bayesian Optimization

• URL: http://arxiv.org/abs/2509.21661v1
• Date: Thu, 25 Sep 2025 22:31:15 GMT
• Title: Automating Sensor Characterization with Bayesian Optimization
• Authors: J. Cuevas-Zepeda, C. Chavez, J. Estrada, J. Noonan, B. D. Nord, N. Saffold, M. Sofo-Haro, R. Spinola e Castro, S. Trivedi,
• Abstract summary: We present a technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 calibration 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.

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