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Electron-tracking Compton camera, which is a complete Compton camera withtracking Compton scattering electron by a gas micro time projection chamber, isexpected to open up MeV gamma-ray astronomy. The technical challenge forachieving several degrees of the point spread function is the precisedetermination of the electron-recoil direction and the scattering position fromtrack images. We attempted to reconstruct these parameters using convolutionalneural networks. Two network models were designed to predict the recoildirection and the scattering position. These models marked 41$~$degrees of theangular resolution and 2.1$~$mm of the position resolution for 75$~$keVelectron simulation data in Argon-based gas at 2$~$atm pressure. In addition,the point spread function of ETCC was improved to 15$~$degrees from22$~$degrees for experimental data of 662$~$keV gamma-ray source. Theseperformances greatly surpassed that using the traditional analysis.

Development of Convolutional Neural Networks for an Electron-Tracking Compton Camera