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Nuclear reactors are a source of electron antineutrinos due to the presence of unstable fission products that undergo β - decay. They will be exploited by the JUNO experiment to determine the neutrino mass ordering and to get very precise measurements of the neutrino oscillation parameters. This requires the reactor antineutrino spectrum to be characterized as precisely as possible both through high-resolution measurements, as foreseen by the TAO experiment, and detailed simulation models. In this paper, we present a benchmark analysis utilizing Serpent Monte Carlo simulations in comparison with real pressurized water reactor spent fuel data. Our objective is to study the accuracy of fission fraction predictions as a function of different reactor simulation approximations. Then, using the BetaShape software, we construct reactor antineutrino spectrum using the summation method, thereby assessing the influence of simulation uncertainties on it.

Although the existence of dark matter is supported by many evidences, based on astrophysical measurements, its nature is still completely unknown. One major candidate is represented by weakly interacting massive particles (WIMPs), which could in principle be detected through their collisions with ordinary nuclei in a sensitive target, producing observable low-energy (<100 keV) nuclear recoils. The DarkSide program aims at the WIPMs detection using a liquid argon time projection chamber (LAr-TPC). In this paper we quickly review the DarkSide program focusing in particular on the next generation experiment DarkSide-G2, a 3.6-ton LAr-TPC. The different detector components are described as well as the improvements needed to scale the detector from DarkSide-50 (50 kg LAr-TPC) up to DarkSide-G2. Finally, the preliminary results on background suppression and expected sensitivity are presented.

DarkSide is a multi-stage program devoted to direct searches of Dark Matter particles with detectors based on double phase liquid Argon Time Projection Chamber. The DarkSide-50 setup is running underground at the Laboratori Nazionali del Gran Sasso. First it was operated with Atmospheric Argon and during that run (1422 ± 67) kg×d of truly background-free exposure has been accumulated. Obtained data made it possible to set a 90% C.L. upper limit on the WIMP-nucleon cross section of 6.1 × 10−44 cm2 (for a WIMP mass of 100 GeV/c2). Presently the detector is filled with Underground Argon, which is depleted in 39Ar by a factor of (1.4 ± 0.2)×103 with respect to Atmospheric Argon. Acquired so far (2616 ± 43) kg×d (71 live days) in combination with the data from the Atmospheric Argon run give us the 90% C.L. upper limit on the WIMP-nucleon spin-independent cross section of 2.0×10−44 cm2 for a WIMP mass of 100 GeV/c2. Up to date this is the best limit obtained with an argon target.

The DarkSide program at LNGS aims to perform background-free WIMP searches using two phase liquid argon time projection chambers, with the ultimate goal of covering all parameters down to the so-called neutrino floor. One of the distinct features of the program is the use of underground argon with has a reduced content of the radioactive 39Ar compared to atmospheric argon. The DarkSide Collaboration is currently operating the DarkSide-50 experiment, the first such WIMP detector using underground argon. Operations with underground argon indicate a suppression of 39Ar by a factor (1.4 ± 0.2) × 103 relative to atmospheric argon. The new results obtained with DarkSide-50 and the plans for the next steps of the DarkSide program, the 20t fiducial mass DarkSide-20k detector and the 200 t fiducial Argo, are reviewed in this proceedings.

The DarkSide experiment is designed for the direct detection of Dark Matter with a double phase liquid Argon TPC operating underground at Laboratori Nazionali del Gran Sasso. The TPC is placed inside a 30 tons liquid organic scintillator sphere, acting as a neutron veto, which is in turn installed inside a 1 kt water Cherenkov detector. The current detector is running since November 2013 with a 50 kg atmospheric Argon fill and we report here the first null results of a Dark Matter search for a (1422 ± 67) kg.d exposure. This result correspond to a 90% CL upper limit on the WIMP-nucleon cross section of 6.1 × 10-44 cm2 (for a WIMP mass of 100 GeV c2) and it's currently the most sensitive limit obtained with an Argon target.

DarkSide-50 is a dark matter detection experiment searching for Weakly Interacting Massive Particles (WIMPs), in Gran Sasso National Laboratory. For experiments like DarkSide-50, neutrons are one of the primary backgrounds that can mimic WIMP signals. The experiment consists of three nested detectors: a liquid argon time projection chamber surrounded by two outer detectors. The outermost detector is a 10 m by 11 m cylindrical water Cherenkov detector with 80 PMTs, designed to provide shielding and muon vetoing. Inside the water Cherenkov detector is the 4 m diameter spherical boron-loaded liquid scintillator veto, with a cocktail of pseudocumene, trimethyl borate, and PPO wavelength shifter, designed to provide shielding, neutron vetoing, and in situ measurements of the TPC backgrounds. We present design and performance details of the DarkSide-50 outer detectors.

We report the direct measurement of the 7Be solar neutrino signal rate performed with the Borexino detector at the Laboratori Nazionali del Gran Sasso. The interaction rate of the 0.862 MeV 7Be neutrinos is 49±3stat±4syst counts/(day·100 ton). The hypothesis of no oscillation for 7Be solar neutrinos is inconsistent with our measurement at the 4σ C.L. Our result is the first direct measurement of the survival probability for solar νe in the transition region between matter-enhanced and vacuum-driven oscillations. The measurement improves the experimental determination of the flux of 7Be, pp, and CNO solar νe, and the limit on the effective neutrino magnetic moment using solar neutrinos.

DarkSide-50 at Gran Sasso underground laboratory (LNGS), Italy, is a direct dark matter search experiment based on a liquid argon TPC. DS-50 has completed its first dark matter run using atmospheric argon as target. The detector performances and the results of the first physics run are presented in this proceeding.

JUNO is a huge neutrino detector that will use 20 kton of organic liquid scintillator as its detection medium. The scintillator is a mixture of linear alkyl benzene (LAB), 2.5 g/L of 2,5-diphenyloxazole (PPO) and 3 mg/L of 1,4-Bis(2-methylstyryl)benzene (Bis-MSB). The main goal of JUNO is to determine the Neutrino Mass Ordering [1, 2, 3]. In order to achieve this purpose, good energy and position reconstruction is required, hence a complete understanding of the optical characteristics of the liquid scintillator is mandatory. In this paper we present the measurements on the JUNO scintillator emission spectrum, absorption length and fluorescence time distribution performed respectively with a spectrofluorimeter, a spectrophotometer and a custom made setup

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 this context, inverse beta decay represents the golden channel for antineutrino detection, providing a pair of correlated events, thus a strong experimental signature to distinguish the signal from a variety of backgrounds. However, given the low cross-section of antineutrino interactions, the development of a powerful event selection algorithm becomes imperative to achieve effective discrimination between signal and backgrounds. In this study, we introduce a machine learning (ML) model to achieve this goal: a fully connected neural network as a powerful signal-background discriminator for a large liquid scintillator detector. We demonstrate, using the JUNO detector as an example, that, despite the already high efficiency of a cut-based approach, the presented ML model can further improve the overall event selection efficiency. Moreover, it allows for the retention of signal events at the detector edges that would otherwise be rejected because of the overwhelming amount of background events in that region. We also present the first interpretable analysis of the ML approach for event selection in reactor neutrino experiments. This method provides insights into the decision-making process of the model and offers valuable information for improving and updating traditional event selection approaches.