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.
@article{arxiv.2509.21661,
title = {Automating Sensor Characterization with Bayesian Optimization},
author = {J. Cuevas-Zepeda and C. Chavez and J. Estrada and J. Noonan and B. D. Nord and N. Saffold and M. Sofo-Haro and R. Spinola e Castro and S. Trivedi},
journal= {arXiv preprint arXiv:2509.21661},
year = {2026}
}