Search for: All records

Borexino could efficiently distinguish between $\alpha$and $\beta$radiation in its liquid scintillator by the characteristic time profile of its scintillation pulse. This $\alpha /\beta$discrimination, first demonstrated on the ton scale in the counting test facility prototype, was used throughout the lifetime of the experiment between 2007 and 2021. With this method, the $\alpha$events are identified and subtracted from the solar neutrino events similar to $\beta$. This is particularly important in liquid scintillators, as the $\alpha$scintillation is strongly quenched. In Borexino, the prominent ${}^{210}\mathrm{Po}$decay peak was a background in the energy range of electrons scattered from ${}^7\mathrm{Be}$solar neutrinos. Optimal $\alpha /\beta$discrimination was achieved with a , with a higher ability to leverage the timing information of the scintillation photons detected by the photomultiplier tubes. An event-by-event, high efficiency, stable, and uniform pulse shape discrimination was essential in characterizing the spatial distribution of background in the detector. This benefited most Borexino measurements, including solar neutrinos in the $pp$chain and the first direct observation of the CNO cycle in the Sun. This paper presents key milestones in $\alpha /\beta$discrimination in Borexino as a term of comparison for current and future large liquid scintillator detectors. Published by the American Physical Society2024 

Abstract The search for neutrino events in correlation with gravitational wave (GW) events for three observing runs (O1, O2 and O3) from 09/2015 to 03/2020 has been performed using the Borexino data-set of the same period. We have searched for signals of neutrino-electron scattering and inverse beta-decay (IBD) within a time window of$$\pm \, 1000$$ $\pm 1000$ s centered at the detection moment of a particular GW event. The search was done with three visible energy thresholds of 0.25, 0.8 and 3.0 MeV. Two types of incoming neutrino spectra were considered: the mono-energetic line and the supernova-like spectrum. GW candidates originated by merging binaries of black holes (BHBH), neutron stars (NSNS) and neutron star and black hole (NSBH) were analyzed separately. Additionally, the subset of most intensive BHBH mergers at closer distances and with larger radiative mass than the rest was considered. In total, follow-ups of 74 out of 93 gravitational waves reported in the GWTC-3 catalog were analyzed and no statistically significant excess over the background was observed. As a result, the strongest upper limits on GW-associated neutrino and antineutrino fluences for all flavors ($$\nu _e, \nu _\mu , \nu _\tau $$ ${\nu }_e,{\nu }_{\mu },{\nu }_{\tau }$) at the level$$10^9{-}10^{15}~\textrm{cm}^{-2}\,\textrm{GW}^{-1}$$ ${10}^9-{10}^{15}{\text{cm}}^{-2}{\text{GW}}^{-1}$have been obtained in the 0.5–5 MeV neutrino energy range. 

Abstract The search for neutrino events in correlation with 42 most intense fast radio bursts (FRBs) has been performed using the Borexino dataset from 05/2007 to 06/2021. We have searched for signals with visible energies above 250 keV within a time window of $$\pm \, 1000$$ ± 1000  s corresponding to detection time of a particular FRB. We also applied an alternative approach based on searching for specific shapes of neutrino-electron scattering spectra in the full exposure data of the Borexino detector. In particular, two incoming neutrino spectra were considered: the monoenergetic line and the spectrum expected from supernovae. The same spectra were considered for electron antineutrinos detected through inverse beta-decay reaction. No statistically significant excess over the background was observed. As a result, the strongest upper limits on FRB-associated neutrino fluences of all flavors have been obtained in the 0.5–50 MeV neutrino energy range. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.