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Deep Convolutional Neural Networks (CNNs) have exhibited remarkable eficacy in data analysis across various domains of physical research. Within the realm of particle and high-energy physics, an imperative challenge revolves around the scrutiny of particle tracks acquired through track detectors, exemplified by liquefied argon time projection chambers (LArTPCs). These cutting-edge neural networks have showcased unparalleled performance in tackling this intricate task, demonstrating their profound potential in enhancing particle track analysis. Since the scalability requirement is imposed on neutrino detectors, it is also imposed on CNN model implementations. The scalability for LArTPC data processing is achieved due to the sparsity of the data, which have the form of thin trajectories. To process sparse data, subspecies sparse convolutional networks (SSCNs) and sparse tensor networks (STNs) have been proposed. In this paper, we present the results of semantic segmentation of the LArTPC-simulated PILArNET data using STNs and test various modifications of the classical U-Net architecture: Attention U-ResNet, U-ResNet3+, U-ResNet with an additional deep supervision block, as well as loss functions: focal loss, balanced cross-entropy to improve the accuracy of Michel electron identification. The best results were obtained for balanced cross-entropy. Further improvement is possible by combining several methods. [ABSTRACT FROM AUTHOR]

Possibility to use carbon as a target material to generate fast neutrons from D(d, n)3He nuclear reaction and intermediate energy neutrons from 12C(d, n)13N nuclear reaction was studied. The experiment results were obtained to characterize an adsorption of the deuterium while stuffing the carbon target by beam of accelerated ions and a desorption of the deuterium from the same carbon target as the function of its temperature. Bombardment of carbon target with 400 keV deuteron beam of 1 mA current leads to the 12C(d, n)13N nuclear reaction, allowing to generate neutrons of (10 100) keV energy range with neutron flux density (106 - 107) n/(cm2·s) near the carbon target. [ABSTRACT FROM AUTHOR]

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Copyright of Nuclear Physics & Atomic Energy is the property of National Academy of Sciences of Ukraine, Institute for Nuclear Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)