activity
20052026
most citedIndication for the disappearance of reactor electron antineutrinos in the Double Chooz experiment

1.2k citations · 8.6k across the 128 of their papers we have counts for

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52 papers · 1 filter

physics.ins-det2026

Performance of a first multi-cell WOM-based liquid scintillator detector as prototype for the SHiP Surrounding Background Tagger

M. Böhles, A. Brignoli, P. Deucher +20

The Search for Hidden Particles (SHiP) Experiment was approved by CERN in 2024. Feebly-interacting particles that are produced in a proton Beam Dump Facility (BDF) will decay in th…

physics.ins-det2026

The Eos detector: a demonstrator of hybrid optical detection technology

Eos Collaboration, S. Arora, M. Askins +64

Eos is an R&D testbed for hybrid detector technologies, featuring state-of-the-art sub-ns photosensors, the first implementation of dichroicons in a large-scale demonstrator, and t…

physics.ins-det2026

Final assessment of radioactive impurities in the JUNO detector

Thomas Adam, Fengpeng An, Costas Andreopoulos +569

The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detect…

physics.ins-det2026

Embedded underwater front-end electronics for the 3-inch photomultipliers in the JUNO experiment

Cédric Cerna, Miao He, Xiaoshan Jiang +591

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) und…

physics.ins-det2025

Doping of a Borexino-like Liquid Scintillator with Tellurium-Diols

Hans Th. J. Steiger, Marco Beretta, Manuel Böhles +8

One promising approach for future neutrinoless double beta decay () searches is the incorporation of candidate isotopes into liquid scintillator detectors. In this work, a sa…

physics.ins-det2024

Model-independent searches of new physics in DARWIN with a semi-supervised deep learning pipeline

J. Aalbers, K. Abe, M. Adrover +231

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-t…