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

Fibronectin promotes directional persistence in fibroblast migration through interactions with both its cell-binding and heparin-binding domains

The precise mechanisms through which insoluble, cell-adhesive ligands induce and regulate directional cell migration remain obscure. We recently demonstrated that elevated surface density of physically adsorbed plasma fibronectin (FN) promotes high directional persistence in fibroblast migration. While cell-FN association through integrins α5β1 and αvβ3 was necessary, substrates that selectively engaged these integrins did not support the phenotype. We here show that high directional persistence necessitates a combination of the cell-binding and C-terminal heparin-binding domains of FN, but does not require the engagement of syndecan-4 or integrin α4β1. FN treatment with various fixation agents indicated that associated changes in fibroblast motility were due to biochemical changes, rather than alterations in its physical state. The nature of the coating determined the ability of fibroblasts to assemble endogenous or exogenous FN, while FN fibrillogenesis played a minor, but significant, role in regulating directionality. Interestingly, knockdown of cellular FN abolished cell motility altogether, demonstrating a requirement for intracellular processes in enabling fibroblast migration on FN. Lastly, kinase inhibition experiments revealed that regulation of cell speed and directional persistence are decoupled. Hence, we have identified factors that render full-length FN a promoter of directional migration and discuss the possible, relevant mechanisms