Bayesian inference proves to be a robust tool for the fitting of parametric models on experimental datasets. In the case of electron kinetics, it can help the identification of non-thermal components in electron population and their relation with plasma parameters and dynamics. We present here a tool for electron distribution reconstruction based on MCMC (Monte Carlo Markov Chain) based Bayesian inference on Thomson Scattering data, discussing the computational performances of different algorithms and information metrics. Along, a possible integration between Soft X-ray spectroscopy and Thomson Scattering is presented, focusing on the parametric optimization of diagnostics spectral channels in different plasma regimes.

Recently, during Collective Thomson Scattering (CTS) measurements at mm-waves aimed at studying the ion dynamics in fusion plasmas, strong signals of scattering of the injected beam with non-CTS origin have been detected. A possible explanation of these signals in terms of parametric decay instabilities (PDIs) of the injected wave with power threshold much lower than previously envisaged by theory was proposed [1, 2]. The experimental activity with CTS diagnostic at FTU is aimed at two purposes: the characterization of the thermal ion distribution function and the investigation of the possible low power PDIs processes foreseen by the recent models. In order to ease the data analysis, a set of data processing tools has been integrated on purpose, with an activity started in 2014. Here we present the last implementation of an integrated data analysis tool, aimed at the investigation of the signals detected with the CTS diagnostic. The last version of the software integrates information included in the raw spectra of scattered radiation with the modeled ECE emission, with the aim of providing calibrated spectra improved with respect to the ones provided up to now. The correct calibration of the signals on the real line of sight of the beams is helpful to better distinguish anomalous emissions from less powerful CTS radiation. In addition, the analysis tool compares the calibrated spectra with the ones predicted considering real scattering parameters evaluated by means of realistic beam-trajectories, changing during the pulse, allowing also to extract information on ion dynamics and plasma composition. The last version of the software, which takes into account also multi-reflection beam-tracing simulations in both polarization modes in support to the scattering experiments, is presented.

Due to the well-known problem of 3He shortage, a series of different thermal neutron detectors alternative to helium tubes are being developed, with the goal to find valid candidates for detection systems for the future spallation neutron sources such as the European Spallation Source (ESS). A possible 3He-free detector candidate is a charged particle detector equipped with a three dimensional neutron converter cathode (3D-C). The 3D-C currently under development is composed by a series of alumina (Al2O3) lamellas coated by 1 ?m of 10B enriched boron carbide (B4C). In order to obtain a good characterization in terms of detector efficiency and uniformity it is crucial to know the thickness, the uniformity and the atomic composition of the B4C neutron converter coating. In this work a non-destructive technique for the characterization of the lamellas that will compose the 3D-C was performed using neutron radiography. The results of these measurements show that the lamellas that will be used have coating uniformity suitable for detector applications. This technique (compared with SEM, EDX, ERDA, XPS) has the advantage of being global (i.e. non point-like) and non-destructive, thus it is suitable as a check method for mass production of the 3D-C elements.

Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess whether all the information available in the data has been exploited or whether there are still margins for improvement of the tools being developed. In this paper, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.

In thermonuclear plasmas, emission tomography uses integrated measurements along lines of sight (LOS) to determine the two-dimensional (2-D) spatial distribution of the volume emission intensity. Due to the availability of only a limited number views and to the coarse sampling of the LOS, the tomographic inversion is a limited data set problem. Several techniques have been developed for tomographic reconstruction of the 2-D gamma and neutron emissivity on JET. In specific experimental conditions the availability of LOSs is restricted to a single view. In this case an explicit reconstruction of the emissivity profile is no longer possible. However, machine learning classification methods can be used in order to derive the type of the distribution. In the present approach the classification is developed using the theory of belief functions which provide the support to fuse the results of independent clustering and supervised classification. The method allows to represent the uncertainty of the results provided by different independent techniques, to combine them and to manage possible conflicts.

In the last years, new and more sophisticated measurements have been at the basis of the major progress in various disciplines related to the environment, such as remote sensing and thermonuclear fusion. To maximize the effectiveness of the measurements, new data analysis techniques are required. First data processing tasks, such as filtering and fitting, are of primary importance, since they can have a strong influence on the rest of the analysis. Even if Support Vector Regression is a method devised and refined at the end of the 90s, a systematic comparison with more traditional non parametric regression methods has never been reported. In this paper, a series of systematic tests is described, which indicates how SVR is a very competitive method of non-parametric regression that can usefully complement and often outperform more consolidated approaches. The performance of Support Vector Regression as a method of filtering is investigated first, comparing it with the most popular alternative techniques. Then Support Vector Regression is applied to the problem of non-parametric regression to analyse Lidar surveys for the environments measurement of particulate matter due to wildfires. The proposed approach has given very positive results and provides new perspectives to the interpretation of the data.

Over the last decade different phase contrast approaches have been exploited at the medical beamline SYRMEP of the synchrotron radiation facility Elettra in Trieste, Italy. In particular special focus has been drawn to analyzer based imaging and the associated imaging theory and processing. Analyzer based Imaging (ABI) and Diffraction Enhanced Imaging (DEI) techniques have been successfully applied in several biomedical applications. Recently it has been suggested to translate the acquired knowledge in this field towards a Thomson Backscattering Source (TBS), which is presently under development at the Frascati National Laboratories of INFN (Istituto Nazionale di Fisica Nucleare) in Rome, Italy. Such source is capable of producing intense and quasi-monochromatic hard X-ray beams. For the technical implementation of biomedical phase imaging at the TBS a grating interferometer for differential phase contrast imaging has been designed and successfully tested at SYRMEP beamline.