Physics Opportunities with Meson Beams

Over the past two decades, meson photo- and electro-production data of unprecedented quality and quantity have been measured at electromagnetic facilities worldwide. By contrast, the meson-beam data for the same hadronic final states are mostly outdated and largely of poor quality, or even nonexistent, and thus provide inadequate input to help interpret, analyze, and exploit the full potential of the new electromagnetic data. To reap the full benefit of the high-precision electromagnetic data, new high-statistics data from measurements with meson beams, with good angle and energy coverage for a wide range of reactions, are critically needed to advance our knowledge in baryon and meson spectroscopy and other related areas of hadron physics. To address this situation, a state of-the-art meson-beam facility needs to be constructed. The present paper summarizes unresolved issues in hadron physics and outlines the vast opportunities and advances that only become possible with such a facility.Comment: 46 pages, 10 figures, 4 table

A Search for ϕ\phi Meson Nucleus Bound State Using Antiproton Annihilation on Nucleus

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Beam and SKS spectrometers at the K1.8 beam line

High-resolution spectrometers for both incident beams and scattered particles have been constructed at the K1.8 beam line of the Hadron Experimental Facility at J-PARC. A point-to-point optics is realized between the entrance and exit of QQDQQ magnets for the beam spectrometer. Fine-pitch wire chamber trackers and hodoscope counters are installed in the beam spectrometer to accept a high rate beam up to 107 Hz. The superconducting kaon spectrometer for scattered particles was transferred from KEK with modifications to the cryogenic system and detectors. A missing-mass resolution of 1.9 ± 0.1 MeV/c2 (FWHM) was achieved for the ∑ peaks of (π±, K+) reactions on a proton target in the first physics run of E19 in 2010

12GeV p + A ハンノウ ニ オケル ロー オヨビ オメガ チュウカンシ ノ カク ブッシツ ニ ヨル シツリョウ ヘンカ ノ ジッケンテキ ケンキュウ

京都大学0048新制・課程博士博士(理学)甲第12091号理博第2985号新制||理||1445(附属図書館)23927UT51-2006-J86京都大学大学院理学研究科物理学・宇宙物理学専攻(主査)教授 今井 憲一, 教授 谷森 達, 助教授 齊藤 直人学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDA

Heuristic model for configurable polymer wire synaptic devices

Recently, there has been considerable research on nonvolatile analog devices for artificial intelligence (AI); however, it focuses on all-coupled neural networks. In contrast, polymer wire-type synaptic devices, which can be expected to be arbitrarily wired similar to a biological neural network, have already been proposed and demonstrated. In this study, we model a polymer wire synaptic device based on the results of previous research, and demonstrate an example of applying simple perceptron (AI) to the model. The results of our study show that it is possible to predict effective methods of using polymer wire synaptic elements in AI

Evolving conductive polymer neural networks on wetware

Neural networks in the brain are structured in three-dimensional (3D) space, and the networks evolve through development and learning, whereas two-dimensional (2D) crossbars have essentially been optimized for a fully connected neural network, which results in a significant increase in unused memristors. Here, we present a prototype of molecular neural networks on wetware consisting of a space-free synaptic medium immersed in monomer solution. In the medium, conductive polymer wires are grown between multiple electrodes through learning only when necessary, i.e. no polymer wire is pre-placed, unlike present 2D crossbar devices. Through experiments, we found the necessary growth conditions for synaptic polymer wires. We first demonstrated the learning of simple Boolean functions and then data-encoding tasks by using our system comprising the synaptic media and their external controllers. These results are valuable for expanding the concept of space-free synapse development, i.e. extending our 2D synaptic media to 3D is possible in principle

Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli