Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information

International audienceGaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information

Incorporating magnetic equilibrium information in Gaussian process tomography for soft X-ray spectroscopy at WEST

Paper published as part of the Proceedings of the 22nd Topical Conference on High-Temperature Plasma Diagnostics, San Diego, California, April 2018International audienceGaussian process tomography (GPT) [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and D. Li, J. Svensson, H. Thomsen, F. Medina, A. Werner, and R. Wolf, Rev. Sci. Instrum. 84, 083506 (2013)] is a recently developed tomography method applied earlier to soft X-ray (SXR) spectroscopy on WEST---Tungsten (W) Environment in Steady-state Tokamak. The short execution time of the algorithm makes GPT an important candidate for providing real-time information on impurity transport and for fast MHD control. In earlier work, GPT has shown its flexibility by providing good reconstruction results without background information about the magnetic equilibrium. On the other hand, information about the magnetic flux surface geometry can in general be useful for additional regularization of the solution. In this paper, we develop a way to take into account the equilibrium information, by constructing a covariance matrix of the prior Gaussian process depending on the flux surface geometry. The GPT method is validated using synthetic SXR emissivity profiles relevant to WEST plasmas and compares favorably with the classical algorithm based on minimization of the Fisher information

Comparison of two regularization methods for Soft x-ray tomography at Tore Supra

International audienceSoft x-ray (SXR) emission in the range 0.1-20 keV is widely used to obtain valuable information on tokamak plasma physics, such as particle transport, magnetic configuration or magnetohydrodynamic activity. In particular, 2D tomography is the usual plasma diagnostic to access the local SXR emissivity. The tomographic inversion is traditionally performed from lineintegrated measurements of two or more cameras viewing the plasma in a poloidal cross-section, like at Tore Supra (TS). Unfortunately, due to the limited number of measured projections and presence of noise, the tomographic reconstruction of SXR emissivity is a mathematical ill-posed problem. Thus, obtaining reliable results of the tomographic inversion is a very challenging task. In order to perform the reconstruction, inversion algorithms implemented in present tokamaks use a priori information as additional constraints imposed on the plasma SXR emissivity. Among several potential inversion methods, some of them have been identified as well suited to tokamak plasmas. The purpose of this work is to compare two promising inversion methods, i.e. the minimum fisher information method already used at TS and planned for WEST configuration, and the alternative 2nd order Phillips-Tikhonov regularization with smoothness constraints imposed on the second derivative norm. Respective accuracy of both reconstruction methods as well as overall robustness and computational time are studied, using several synthetic SXR emissivity profiles. Finally, a real case is studied through tomographic reconstruction from TS SXR database

Synthesizing realistic neural population activity patterns using generative adversarial networks

The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain. We demonstrate that our proposed GAN, which we termed Spike-GAN, generates spike trains that match accurately the first- and second-order statistics of datasets of tens of neurons and also approximates well their higher-order statistics. We applied Spike-GAN to a real dataset recorded from salamander retina and showed that it performs as well as state-of-the-art approaches based on the maximum entropy and the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not require to specify a priori the statistics to be matched by the model, and so constitutes a more flexible method than these alternative approaches. Finally, we show how to exploit a trained Spike-GAN to construct’importance maps’ to detect the most relevant statistical structures present in a spike train. Spike-GAN provides a powerful, easy-to-use technique for generating realistic spiking neural activity and for describing the most relevant features of the large-scale neural population recordings studied in modern systems neuroscience

A new class of indicators for the model selection of scaling laws in nuclear fusion

The development of computationally efficient model selection strategies represents an important problem facing the analysis of Nuclear Fusion experimental data, in particular in the field of scaling laws for the extrapolation to future machines, and image processing. In this paper, a new model selection indicator, named Model Falsification Criterion (MFC), will be presented and applied to the problem of choosing the most generalizable scaling laws for the power threshold to access the H-mode of confinement in Tokamaks. The proposed indicator is based on the properties of the model residuals, their entropy and an implementation of the data falsification principle. The model selection ability of the proposed criterion will be demonstrated in comparison with the most widely used frequentist (Akaike Information Criterion) and bayesian (Bayesian Information Criterion) indicators.Comment: 4 pages, 2 figure

Polycapillary optics for soft X-ray imaging and tomography

Magnetic plasmas are extended volumetric sources of X-rays, and these emissions could reveal a lot of information about the processes occurring into the plasmas. Unfortunately, the constraints posed by these toroidal devices (high neutron flux, gamma and hard-X background, extremely high radiofrequency powers, high magnetic fields, optical limitations and so on) are very severe and limit strongly the possibility to install X-ray detectors directly into or close to the machine. Soft X-ray diagnostics are meant both as tomography and imaging. We started, therefore, to investigate the feasibility of using polycapillary optics for these purposes, in collaboration between Istituto Nazionale di Fisica Nucleare (INFN)- Frascati, Ente per le Nuove tecnologie, l’Energia e l’Ambiente (ENEA)-Frascati and the Commissariat de l’Energie Atomique (CEA)-Cadarache. The first tests were performed in order to characterize the polycapillary lenses (convergence, divergence, efficiency, spectral dispersion, etc.) for distances much larger than the optical focal length of the lenses, both for the detector and for the source. A silicon-based C-MOS imager (Medipix 2) has been used as a detector and the micro focus X-ray tubes as point-like sources. Results of these preliminary tests are presented, and the imaging capabilities of a polycapillary lens as well

Lower hybrid counter-current drive experiment in JET

12th International Congress on Plasma Physics, 25-29 October 2004, Nice (France)Lower hybrid current drive has been demonstrated to be an efficient tool to modify the current profile in order to access to high energy confinement regimes. Counter-current drive could be an alternative scenario provided the current drive efficiency is not too small when fast electrons flow in the opposite way to the DC electric field. By reversing the toroidal field (Bt=-3.1T) and the plasma current (Ip=-1.45MA), counter current drive with lower hybrid waves has been investigated for the first time in JET. The experiments were carried out at low plasma density ( =1.0 x1019m-3 , ne(0)=1.6 x 1019m-3) with 2.9MW of lower hybrid power. The CRONOS code[1], which couples the diffusion equations to a 2-D equilibrium code, has been used to estimate the RF driven current. Runs indicate that loop voltage and internal inductance are best simulated with a current drive efficiency of –1.0 x 1019 A.W-1.m-2 with a peaked central LH power deposition deduced from DELPHINE[2]. This efficiency is indeed very close to the one found for co-LHCD at similar plasma current and density. Current profile evolves from a hollow profile (with a minimum at r/a ~0) and a maximum at r/a~0.4-0.5) to a rather flat profile (up to r/a=0.3)

Transcriptional profiling of zebrafish identifies host factors controlling susceptibility to Shigella flexneri

Shigella flexneri is a human-adapted pathovar of Escherichia coli that can invade the intestinal epithelium, causing inflammation and bacillary dysentery. Although an important human pathogen, the host response to S. flexneri has not been fully described. Zebrafish larvae represent a valuable model to study human infections in vivo. Here we use a Shigella-zebrafish infection model to generate mRNA expression profiles of host response to Shigella infection at the whole animal level. Immune response-related processes dominate the signature of early Shigella infection (6 hours post-infection). Consistent with its clearance from the host, the signature of late Shigella infection (24 hours post-infection) is significantly changed, and only a small set of immune-related genes remain differentially expressed, including acod1 and gpr84. Using mutant lines generated by ENU, CRISPR mutagenesis and F0 Crispants, we show that acod1- and gpr84-deficient larvae are more susceptible to Shigella infection. Together, these results highlight the power of zebrafish to model infection by bacterial pathogens and reveal the mRNA expression of the early (acutely infected) and late (clearing) host response to Shigella infection