70 citations · 413 across the 32 of their papers we have counts for
6 papers · 1 filter
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
Lorenzo Borella, Alberto Coppi, Jacopo Pazzini +4
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning…
Distillation and Stripping purification plants for JUNO liquid scintillator
C. Landini, M. Beretta, P. Lombardi +64
The optical and radiochemical purification of the scintillating liquid, which will fill the central detector of the JUNO experiment, plays a crucial role in achieving its scientifi…
Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector
A. Gavrikov, V. Cerrone, A. Serafini +65
Several neutrino detectors, KamLAND, Daya Bay, Double Chooz, RENO, and the forthcoming large-scale JUNO, rely on liquid scintillator to detect reactor antineutrino interactions. In…
Refractive index in the JUNO liquid scintillator
H. S. Zhang, M. Beretta, S. Cialdi +73
In the field of rare event physics, it is common to have huge masses of organic liquid scintillator as detection medium. In particular, they are widely used to study neutrino prope…
Potential to identify neutrino mass ordering with reactor antineutrinos at JUNO
JUNO Collaboration, Angel Abusleme, Thomas Adam +622
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO's sen…
JUNO Sensitivity to Invisible Decay Modes of Neutrons
JUNO Collaboration, Angel Abusleme, Thomas Adam +651
We explore the decay of bound neutrons into invisible particles (e.g., or ) in the JUNO liquid scintillator detector, which do not produce an…