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Andreotti, M. et al, Cervera-Villanueva, A., Garcia-Peris, M. a., Martin-Albo, J., Querol, M., Rocabado, J., et al. (2024). Cryogenic characterization of Hamamatsu HWB MPPCs for the DUNE photon detection system. J. Instrum., 19(1), T01007–27pp.
Abstract: The Deep Underground Neutrino Experiment (DUNE) is a next generation experiment aimed to study neutrino oscillation. Its long-baseline configuration will exploit a Near Detector (ND) and a Far Detector (FD) located at a distance of similar to 1300 km. The FD will consist of four Liquid Argon Time Projection Chamber (LAr TPC) modules. A Photon Detection System (PDS) will be used to detect the scintillation light produced inside the detector after neutrino interactions. The PDS will be based on light collectors coupled to Silicon Photomultipliers (SiPMs). Different photosensor technologies have been proposed and produced in order to identify the best samples to fullfill the experiment requirements. In this paper, we present the procedure and results of a validation campaign for the Hole Wire Bonding (HWB) MPPCs samples produced by Hamamatsu Photonics K.K. (HPK) for the DUNE experiment, referring to them as 'SiPMs'. The protocol for a characterization at cryogenic temperature (77 K) is reported. We present the down-selection criteria and the results obtained during the selection campaign undertaken, along with a study of the main sources of noise of the SiPMs including the investigation of a newly observed phenomenon in this field.
Keywords: Cryogenic detectors; Photon detectors for UV, visible and IR photons (solid-state); Photon detectors for UV, visible and IR photons (solid-state) (PIN diodes, APDs, Si-PMTs, G-APDs, CCDs, EBCCDs, EMCCDs, CMOS imagers, etc)
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CALICE Collaboration(Lai, S. et al), & Irles, A. (2024). Software compensation for highly granular calorimeters using machine learning. J. Instrum., 19(4), P04037–28pp.
Abstract: A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
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