In this work, we present novel Machine Learning and Differentiable Programming enhanced calibration techniques used to improve the energy resolution of the Silicon Drift Detectors (SDDs) of the VIP-2 underground experiment at the Gran Sasso National Laboratory (LNGS). We achieve for the first 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 resolution is a key parameter in the VIP-2 experiment, which is dedicated to searches for physics beyond the standard quantum theory, targeting Pauli Exclusion Principle (PEP) violating atomic transitions. Additionally, we show that this method can correct for potential miscalibrations, requiring less fine-tuning with respect to standard methods.
@article{arxiv.2305.17153,
title = {Novel Machine Learning and Differentiable Programming Techniques applied to the VIP-2 Underground Experiment},
author = {F Napolitano and M Bazzi and M Bragadireanu and M Cargnelli and A Clozza and L De Paolis and R Del Grande and C Fiorini and C Guaraldo and M Iliescu and M Laubenstein and S Manti and J Marton and M Miliucci and K Piscicchia and A Porcelli and A Scordo and F Sgaramella and D Sirghi and F Sirghi and O Doce and J Zmeskal and C Curceanu},
journal= {arXiv preprint arXiv:2305.17153},
year = {2023}
}