Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6  ×  6  ×  6 m 3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Larval excretory-secretory products from the parasite Schistosoma mansoni modulate HSP70 protein expression in defence cells of its snail host, Biomphalaria glabrata

Synthesis of heat shock proteins (HSPs) following cellular stress is a response shared by many organisms. Amongst the HSP family, the ∼70 kDa HSPs are the most evolutionarily conserved with intracellular chaperone and extracellular immunoregulatory functions. This study focused on the effects of larval excretory-secretory products (ESPs) from the parasite Schistosoma mansoni on HSP70 protein expression levels in haemocytes (defence cells) from its snail intermediate host Biomphalaria glabrata. S. mansoni larval stage ESPs are known to interfere with haemocyte physiology and behaviour. Haemocytes from two different B. glabrata strains, one which is susceptible to S. mansoni infection and one which is resistant, both showed reduced HSP70 protein levels following 1 h challenge with S. mansoni ESPs when compared to unchallenged controls; however, the reduction observed in the resistant strain was less marked. The decline in intracellular HSP70 protein persisted for at least 5 h in resistant snail haemocytes only. Furthermore, in schistosome-susceptible snails infected by S. mansoni for 35 days, haemocytes possessed approximately 70% less HSP70. The proteasome inhibitor, MG132, partially restored HSP70 protein levels in ESP-challenged haemocytes, demonstrating that the decrease in HSP70 was in part due to intracellular degradation. The extracellular signal-regulated kinase (ERK) signalling pathway appears to regulate HSP70 protein expression in these cells, as the mitogen-activated protein-ERK kinase 1/2 (MEK1/2) inhibitor, U0126, significantly reduced HSP70 protein levels. Disruption of intracellular HSP70 protein expression in B. glabrata haemocytes by S. mansoni ESPs may be a strategy employed by the parasite to manipulate the immune response of the intermediate snail host

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

AbstractLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation.</jats:p