SciPost Submission Page
From real-time calibrations to smart HV tuning for FAIR
by Valentin Kladov, Johan Messchendorp, James Ritman
Submission summary
| Authors (as registered SciPost users): | Valentin Kladov |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2509.17653v1 (pdf) |
| Code repository: | https://github.com/KladovValentin/drogonapp |
| Date submitted: | Sept. 23, 2025, 8:44 a.m. |
| Submitted by: | Valentin Kladov |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
Overleaf Writefull, GPT5: Text cleanup suggestions and grammar errors corrections
Abstract
Real-time data processing of the next generation of experiments at FAIR requires reliable event reconstruction and thus depends heavily on in-situ calibration procedures. Previously, we developed a neural-network-based approach that predicts calibration parameters from continuously available environmental and operational data and validated it on the HADES Multiwire Drift Chambers (MDCs), achieving fast predictions as accurate as offline calibrations. In this work, we introduce several methodological improvements that enhance both accuracy and the ability to adapt to new data. These include changes to the input features, better offline calibration and trainable normalizations. Furthermore, by combining beam-time and cosmic-ray datasets, we demonstrate that the learned dependencies can be transferred between very different data-taking scenarios. This enables the network not only to provide real-time calibration predictions, but also to infer optimal high-voltage settings, thus establishing a practical framework for a real-time detector control during data acquisition process.