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Denoising application for electron spectrometer in laser-driven ion acceleration using a Simulation-supervised Learning based CDAE
https://repo.qst.go.jp/records/83794
https://repo.qst.go.jp/records/837940c8d2b9a-c59d-47c1-bdd8-5c29699b6a1a
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2021-01-07 | |||||
| タイトル | ||||||
| タイトル | Denoising application for electron spectrometer in laser-driven ion acceleration using a Simulation-supervised Learning based CDAE | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Miyatake, Tatsuhiko
× Miyatake, Tatsuhiko× Shiokawa, Keiichiro× Sakaki, Hironao× Dover, NicholasPeter× Nishiuchi, Mamiko× Lowe, HazelFrances× Kondo, Kotaro× Kon, Akira× Kando, Masaki× Kondo, Kiminori× Tatsuhiko, Miyatake× Hironao, Sakaki× Dover, NicholasPeter× Mamiko, Nishiuchi× Lowe, HazelFrances× Kotaro, Kondo× Akira, Kon× Masaki, Kando× Kiminori, Kondo |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | 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. | |||||
| 書誌情報 |
Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 巻 999, p. 165227, 発行日 2021-05 |
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| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 0168-9002 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.nima.2021.165227 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/pii/S0168900221002114 | |||||
