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Abstract SBND is the near detector of the Short-Baseline Neutrino program at Fermilab. Its location near to the Booster Neutrino Beam source and relatively large mass will allow the study of neutrino interactions on argon with unprecedented statistics. This paper describes the expected performance of the SBND photon detection system, using a simulated sample of beam neutrinos and cosmogenic particles. Its design is a dual readout concept combining a system of 120 photomultiplier tubes, used for triggering, with a system of 192 X-ARAPUCA devices, located behind the anode wire planes. Furthermore, covering the cathode plane with highly-reflective panels coated with a wavelength-shifting compound recovers part of the light emitted towards the cathode, where no optical detectors exist. We show how this new design provides a high light yield and a more uniform detection efficiency, an excellent timing resolution and an independent 3D-position reconstruction using only the scintillation light. Finally, the whole reconstruction chain is applied to recover the temporal structure of the beam spill, which is resolved with a resolution on the order of nanoseconds. 

Abstract We present a background model for dark matter searches using an array of NaI(Tl) crystals in the COSINE-100 experiment that is located in the Yangyang underground laboratory. The model includes background contributions from both internal and external sources, including cosmogenic radionuclides and surface $$^{210}$$ 210 Pb contamination. To build the model in the low energy region, with a threshold of 1 keV, we used a depth profile of $$^{210}$$ 210 Pb contamination in the surface of the NaI(Tl) crystals determined in a comparison between measured and simulated spectra. We also considered the effect of the energy scale errors propagated from the statistical uncertainties and the nonlinear detector response at low energies. The 1.7 years COSINE-100 data taken between October 21, 2016 and July 18, 2018 were used for this analysis. Our Monte Carlo simulation provides a non-Gaussian peak around 50 keV originating from beta decays of bulk $$^{210}$$ 210 Pb in a good agreement with the measured background. This model estimates that the activities of bulk $$^{210}$$ 210 Pb and $$^{3}$$ 3 H are dominating the background rate that amounts to an average level of $$2.85\pm 0.15$$ 2.85 ± 0.15  counts/day/keV/kg in the energy region of (1–6) keV, using COSINE-100 data with a total exposure of 97.7 kg $$\cdot $$ · years. 

Abstract We report the identification of metastable isomeric states of$$^{228}$$ ${}^{228}$Ac at 6.28 keV, 6.67 keV and 20.19 keV, with lifetimes of an order of 100 ns. These states are produced by the$$\beta $$ $\beta$-decay of$$^{228}$$ ${}^{228}$Ra, a component of the$$^{232}$$ ${}^{232}$Th decay chain, with$$\beta $$ $\beta$Q-values of 39.52 keV, 39.13 keV and 25.61 keV, respectively. Due to the low Q-value of$$^{228}$$ ${}^{228}$Ra as well as the relative abundance of$$^{232}$$ ${}^{232}$Th and their progeny in low background experiments, these observations potentially impact the low-energy background modeling of dark matter search experiments. 

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.