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20052026
most citedA New Measurement of Kaonic Hydrogen X rays

384 citations · 1.3k across the 76 of their papers we have counts for

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25 papers · 1 filter

physics.ins-det2026

Extended X-ray energy characterization of SIDDHARTA-2 large-area Silicon Drift Detectors up to 50 keV

Francesco Clozza, Francesco Sgaramella, Leonardo Abbene +38

The SIDDHARTA-2 experiment at the DANE collider of INFN-LNF performs high precision light kaonic atoms X-ray spectroscopy to investigate the kaon-nucleon(s) strong interaction i…

physics.ins-det2025

New 1mm thick Silicon Drift Detectors for future researches of Kaonic Atoms and the Pauli Exclusion principle

F. Clozza, F. Sgaramella, L. Abbene +33

Kaonic atoms, formed when a negatively charged kaon replaces an electron, provide a sensitive probe of the low-energy strong interaction via precision X-ray spectroscopy. The SIDDH…

physics.ins-det2024

Kaonic lead feasibility measurement at DAΦNE to solve the charged kaon mass discrepancy

D. Bosnar, L. Abbene, C. Amsler +39

An HPGe detector equipped with a transistor reset preamplifier and readout with a CAEN DT5781 fast pulse digitizer was employed in the measurement of X-rays from kaonic lead at the…

physics.ins-det2023★ 1 cited

SIDDHARTA-2 apparatus for kaonic atoms research on the DANE collider

F. Sirghi, F. Sgaramella, L. Abbene +41

SIDDHARTA-2 represents a state-of-the-art experiment designed to perform dedicated measurements of kaonic atoms, which are particular exotic atom configurations composed of a negat…

physics.ins-det2023

CdZnTe detectors tested at the DAΦNE collider for future kaonic atoms measurements

A. Scordo, L. Abbene, F. Artibani +42

The SIDDHARTA-2 collaboration at the INFN Laboratories of Frascati (LNF) aims to perform groundbreaking measurements on kaonic atoms. In parallel and beyond the ongoing kaonic deut…

physics.ins-det2023★ 2 cited

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

F Napolitano, M Bazzi, M Bragadireanu +20

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…