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In this work, we present novel Machine Learning and DifferentiableProgramming enhanced calibration techniques used to improve the energyresolution of the Silicon Drift Detectors (SDDs) of the VIP-2 undergroundexperiment at the Gran Sasso National Laboratory (LNGS). We achieve for thefirst time a Full Width at Half Maximum (FWHM) in VIP-2 below 180 eV at 8 keV,improving around 10 eV on the previous state-of-the-art. SDDs energy resolutionis a key parameter in the VIP-2 experiment, which is dedicated to searches forphysics beyond the standard quantum theory, targeting Pauli Exclusion Principle(PEP) violating atomic transitions. Additionally, we show that this method cancorrect for potential miscalibrations, requiring less fine-tuning with respectto standard methods.

Novel Machine Learning and Differentiable Programming Techniques applied to the VIP-2 Underground Experiment