量研学術機関リポジトリ「QST-Repository」は、国立研究開発法人 量子科学技術研究開発機構に所属する職員等が生み出した学術成果（学会誌発表論文、学会発表、研究開発報告書、特許等）を集積しインターネット上で広く公開するサービスです。 Welcome to QST-Repository where we accumulates and discloses the academic research results(Journal Publications, Conference presentation, Research and Development Report, Patent, etc.) of the members of National Institutes for Quantum Science and Technology.
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In the interaction between intense laser and the target matter, near-100-MeV proton acceleration is demonstrated. It offers that the potential to realize a energy saving compact particle accelerators in the future. To understand the acceleration process mechanism, precise measurement of both the energy spectrum and the two-dimensional distribution is required. Against this background, we have developed a new measurement method for laser-accelerated sub-GeV-class protons using the nuclear emulsion. Based on the Multiple Coulomb Scattering (MCS) method in an Emulsion Cloud Chamber (ECC), which is a stack of nuclear emulsion films and scatterer plates, the incident energies were inversely evaluated by the scattering angle. The proof-of-principle simulation has been conducted with GEANT-4 Monte Carlo code. To analyze the proton tracks, we have applied the deep learning technique to obtain the incident energy from the amount of MCS in each layer of nuclear emulsion. The median of energy determination coefficient is 0.73 with ramp activation function. The coefficients of the present studies are equal or smaller than the conventional regression model, and we are trying to improve the determination coefficient by optimization of the calculation conditions.
Analysis Method of Laser-accelerated Sub-GeV-class Proton Tracks in Emulsion Cloud Chamber using Deep Learning Technique
