レーザー駆動粒子線加速電子線スペクトルの超解像計測に向けた放射線計測手法と先端数理工学の融合

修士（工学

Denoising application for electron spectrometer in laser-driven ion acceleration using a Simulation-supervised Learning based CDAE

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

Denoising technique of an in-line electron energy spectrometer based on the feature filtering

On the experiment for laser-driven ion acceleration at J-KAREN-P[1] with pulse repetition rates of 0.1 Hz, the angular distribution of electron spectrum is diagnosed by multiple electron spectrometers. The electron spectrometer with comprising a bending magnet, a CCD camera and a scintillator is placed in the main vacuum chamber. When the laser focus on the target, many radiations generated in the main vacuum chamber, therefore, CCD camera is exposed to the radiation and distorted the measured images. We need to develop a new technique for removing noise (denoising) from a noisy image and recovering a true measurement image. In recent years, with the development of machine learning methods such as Deep-Learning, "Deep-Learning based Feature filtering" that uses a feature value of an image obtained from machine learning as a base and reproduces a true image from a noisy image is developed[2]. This filtering technique is a new technique of reconstructing a denoising image by separating noise and true data with the feature value of the true-image data. This technique expectes to be effective for the denoising of radiation. In order to demonstrate the feature filtering method, we make a pseudo-measured image generated from an ideal simulation of electron spectroscopy (using as the true-image data), and the noise-image data which make from the measured radiation noise convoluted with the pseudo-measured image (using as the noise-image data). Then, the ideal feature base is obtained by machine learning from the true-image data and the noise-image data, the feature filtering of the actually measured data is verified by using these the ideal feature base. In this report, we show the denoising performance of the feature filtering compared with "median filter" which is generally used for filtering of radiation noise.HEDS202

Ion species discrimination method by linear energy transfer measurement in Fujifilm BAS-SR Imaging Plate

We have developed a novel discrimination methodology to identify ions in multispecies beams with similar charge to mass ratios but different atomic numbers. After an initial separation by charge-to-mass ratio using co-linear electric and magnetic fields, individual ions can be discriminated by considering the Linear Energy Transfer and non-linear detector response of ions irradiating stimulable phosphor plate (Fujifilm imaging plate), by comparison with Monte-Carlo calculation. We apply the method to energetic multispecies laser-driven ion beams and use it to identify silver ions produced by the interaction between a high contrast, high intensity laser pulse and a sub-m silver foil target. We also show that this method can be used to calibrate imaging plate for arbitrary ion species without requiring individual calibration

New algorithm using L1 regularization for measuring electron energy spectra

Retrieving the spectrum of physical radiation from experimental measurements typically involves using a mathematical algorithm to deconvolve the instrument response function from the measured signal. However, in the field of signal processing known as "Source Separation", which refers to the process of computationally retrieving the separate source components that generate an overlapping signal on the detector, the deconvolution process can become an ill-posed problem and crosstalk complicates the separation of the individual sources. To overcome this problem, we have designed a magnetic spectrometer for inline electron energy spectrum diagnosis and developed an analysis algorithm using techniques applicable to the problem of Source Separation. An unknown polychromatic electron spectrum is calculated by sparse coding using a Gaussian basis function and an L1 regularization algorithm with a sparsity constraint. This technique is verified by a specially designed magnetic field electron spectrometer. We use Monte Carlo simulations of the detector response to Maxwellian input energy distributions with electron temperatures of 5.0, 10.0 and 15.0 MeV to show that the calculated sparse spectrum can reproduce the input spectrum with an optimum energy bin width automatically selected by the L1 regularization. The spectra are reproduced with high accuracy of less than 4.0 % error, without an initial value. The technique is then applied to experimental measurements of intense laser accelerated electron beams from solid targets. Our analysis concept of spectral retrieval and automatic optimization of energy bin width by sparse coding could form the basis of a novel diagnostic method for spectroscopy

Enhanced ion acceleration from transparency-driven foils demonstrated at two ultraintense laser facilities

Laser-driven ion sources are a rapidly developing technology producing high energy, high peak current beams. Their suitability for applications, such as compact medical accelerators, motivates development of robust acceleration schemes using widely available repetitive ultraintense femtosecond lasers. These applications not only require high beam energy, but also place demanding requirements on the source stability and controllability. This can be seriously affected by the laser temporal contrast, precluding the replication of ion acceleration performance on independent laser systems with otherwise similar parameters. Here, we present the experimental generation of >60 MeV protons and >30 MeV u-1 carbon ions from sub-micrometre thickness Formvar foils irradiated with laser intensities >1021 Wcm2. Ions are accelerated by an extreme localised space charge field ≳30 TVm-1, over a million times higher than used in conventional accelerators. The field is formed by a rapid expulsion of electrons from the target bulk due to relativistically induced transparency, in which relativistic corrections to the refractive index enables laser transmission through normally opaque plasma. We replicate the mechanism on two different laser facilities and show that the optimum target thickness decreases with improved laser contrast due to reduced pre-expansion. Our demonstration that energetic ions can be accelerated by this mechanism at different contrast levels relaxes laser requirements and indicates interaction parameters for realising application-specific beam delivery