Details

Autor(en) / Beteiligte

• Napolitano, Fabrizio
• Bazzi, Massimiliano
• Bragadireanu, Mario
• Cargnelli, Michael
• Clozza, Alberto
• De Paolis, Luca
• Del Grande, Raffaele
• Fiorini, Carlo
• Guaraldo, Carlo
• Iliescu, Mihail
• Laubenstein, Matthias
• Manti, Simone
• Marton, Johann
• Miliucci, Marco
• Piscicchia, Kristian
• Porcelli, Alessio
• Scordo, Alessandro
• Sgaramella, Francesco
• Laura Sirghi, Diana
• Sirghi, Florin
• Vazquez Doce, Oton
• Zmeskal, Johann
• Curceanu, Catalina

Titel

Novel machine learning and differentiable programming techniques applied to the VIP-2 underground experiment

Ist Teil von

• Measurement science & technology, 2024-02, Vol.35 (2), p.25501

Erscheinungsjahr

2024

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Abstract 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. We achieve for the first time a full width at half maximum 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 violating atomic transitions. Additionally, we show that this method can correct for potential miscalibrations, requiring less fine-tuning with respect to standard methods.