Speaker
Description
Monolithic Water Cherenkov Neutrino detectors are crucial for understanding neutrino astrophysics and oscillations. Traditional calibration involves analyzing calibration data sequentially, which may overlook parameter correlations and necessitates frequent retuning of reconstruction algorithms. This leads to duplicated efforts and increased detector-related uncertainties in next-generation experiments like Hyper-Kamiokande.
To address this, we propose a machine learning-based approach using a differentiable model of a water Cherenkov simulation for calibration and event reconstruction. We demonstrate how this method allows simultaneous optimization of calibration and reconstruction parameters through gradient descent within a unified framework. Furthermore, we discuss its potential to surpass existing calibration and event reconstruction methods in the near future.
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