Automated Physics-Informed Neural-Networks-Based Calibration of Highly Segmented Silicon Telescopes
Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determination of the identity, energy, and emission angles of reaction products. The increasing granularity of modern silicon telescope arrays enhances experimental capabilities but challenges detector calibration, as conventional channel-by-channel approaches become inefficient and difficult to scale. In this work, we present a fully automated, physics-informed calibration framework based on neural networks, specifically designed for highly segmented silicon detector arrays. The method formulates calibration as a global optimization problem, in which de...
| Authors | M. Rejmund, A. Lemasson, P. Morfouace et al. |
|---|---|
| Primary category | physics.ins-det |
| Published | 15 September 2026 |
| Last updated at source | 5 October 2026 |
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