@article{oai:repo.qst.go.jp:00083794,
 author = {Miyatake, Tatsuhiko and Shiokawa, Keiichiro and Sakaki, Hironao and Dover, NicholasPeter and Nishiuchi, Mamiko and Lowe, HazelFrances and Kondo, Kotaro and Kon, Akira and Kando, Masaki and Kondo, Kiminori and Tatsuhiko, Miyatake and Hironao, Sakaki and Dover, NicholasPeter and Mamiko, Nishiuchi and Lowe, HazelFrances and Kotaro, Kondo and Akira, Kon and Masaki, Kando and Kiminori, Kondo},
 journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},
 month = {May},
 note = {Real experimental measurements in high-radiation environments often suffer from a high-flux of background noise which can limit the retrieval of the underlying signal. It is important to have an effective method to properly remove unwanted noise from measurement images. Machine learning methods using a multilayer neural network (deep learning) have been shown to be effective for extracting features from images. However, the efficacy of such methods is often restricted by a lack of high-quality training data. Here, we demonstrate the application for noise removal by performing simulations to generate virtual training data for a denoising deep-learning model. We first apply the model to simulations of an electron spectrometer measuring the energy spectra of electron beams accelerated from the interaction of an intense laser with a thin foil. By considering the chi-squared test and image test-indexes, namely the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), we found our method to be highly effective. We then used the trained model to denoise real experimental measurements of the electron beam spectra from experiments performed at a state-of-the-art high-power laser facility. This application is offered as a new method for effectively removing noise from experimental data in high-flux radiation background environment.},
 title = {Denoising application for electron spectrometer in laser-driven ion acceleration using a Simulation-supervised Learning based CDAE},
 volume = {999},
 year = {2021}
}