M. Lungaroni

Application of transfer entropy to causality detection and synchronization experiments in tokamaks

Determination of causal-effect relationships can be a difficult task even in the analysis of time series. This is particularly true in the case of complex, nonlinear systems affected by significant ...

How to assess the efficiency of synchronization experiments in tokamaks

Control of instabilities such as ELMs and sawteeth is considered an important ingredient in the development of reactor-relevant scenarios. Various forms of ELM pacing have been tried in the past to influence their behavior using external perturbations. One of the main problems with these synchronization experiments resides in the fact that ELMs are periodic or quasi-periodic in nature. Therefore, after any pulsed perturbation, if one waits long enough, an ELM is always bound to occur. To evaluate the effectiveness of ELM pacing techniques, it is crucial to determine an appropriate interval over which they can have a real influence and an effective triggering capability. In this paper, three independent statistical methods are described to address this issue: Granger causality, transfer entropy and recurrence plots. The obtained results for JET with the ITER-like wall (ILW) indicate that the proposed techniques agree very well and provide much better estimates than the traditional heuristic criteria reported in the literature. Moreover, their combined use allows for the improvement of the time resolution of the assessment and determination of the efficiency of the pellet triggering in different phases of the same discharge. Therefore, the developed methods can be used to provide a quantitative and statistically robust estimate of the triggering efficiency of ELM pacing under realistic experimental conditions.

Advanced signal processing based on support vector regression for lidar applications

The LIDAR technique has recently found many applications in atmospheric physics and remote sensing. One of the main issues, in the deployment of systems based on LIDAR, is the filtering of the backscattered signal to alleviate the problems generated by noise. Improvement in the signal to noise ratio is typically achieved by averaging a quite large number (of the order of hundreds) of successive laser pulses. This approach can be effective but presents significant limitations. First of all, it implies a great stress on the laser source, particularly in the case of systems for automatic monitoring of large areas for long periods. Secondly, this solution can become difficult to implement in applications characterised by rapid variations of the atmosphere, for example in the case of pollutant emissions, or by abrupt changes in the noise. In this contribution, a new method for the software filtering and denoising of LIDAR signals is presented. The technique is based on support vector regression. The proposed new method is insensitive to the statistics of the noise and is therefore fully general and quite robust. The developed numerical tool has been systematically compared with the most powerful techniques available, using both synthetic and experimental data. Its performances have been tested for various statistical distributions of the noise and also for other disturbances of the acquired signal such as outliers. The competitive advantages of the proposed method are fully documented. The potential of the proposed approach to widen the capability of the LIDAR technique, particularly in the detection of widespread smoke, is discussed in detail.

Symbolic regression via genetic programming for data driven derivation of confinement scaling laws without any assumption on their mathematical form

Many measurements are required to control thermonuclear plasmas and to fully exploit them scientifically. In the last years JET has shown the potential to generate about 50 GB of data per shot. These amounts of data require more sophisticated data analysis methodologies to perform correct inference and various techniques have been recently developed in this respect. The present paper covers a new methodology to extract mathematical models directly from the data without any a priori assumption about their expression. The approach, based on symbolic regression via genetic programming, is exemplified using the data of the International Tokamak Physics Activity database for the energy confinement time. The best obtained scaling laws are not in power law form and suggest a revisiting of the extrapolation to ITER. Indeed the best non-power law scalings predict confinement times in ITER approximately between 2 and 3 s. On the other hand, more comprehensive and better databases are required to fully profit from the power of these new methods and to discriminate between the hundreds of thousands of models that they can generate.

Lidar and Dial application for detection and identification: A proposal to improve safety and security

Nowadays the intentional diffusion in air (both in open and confined environments) of chemical contaminants is a dramatic source of risk for the public health worldwide. The needs of a high-tech networks composed by software, diagnostics, decision support systems and cyber security tools are urging all the stakeholders (military, public, research & academic entities) to create innovative solutions to face this problem and improve both safety and security. The Quantum Electronics and Plasma Physics (QEP) Research Group of the University of Rome Tor Vergata is working since the 1960s on the development of laser-based technologies for the stand-off detection of contaminants in the air. Up to now, four demonstrators have been developed (two LIDAR-based and two DIAL-based) and have been used in experimental campaigns during all 2015. These systems and technologies can be used together to create an innovative solution to the problem of public safety and security: the creation of a network composed by detection systems: A low cost LIDAR based system has been tested in an urban area to detect pollutants coming from urban traffic, in this paper the authors show the results obtained in the city of Crotone (south of Italy). This system can be used as a first alarm and can be coupled with an identification system to investigate the nature of the threat. A laboratory dial based system has been used in order to create a database of absorption spectra of chemical substances that could be release in atmosphere, these spectra can be considered as the fingerprints of the substances that have to be identified. In order to create the database absorption measurements in cell, at different conditions, are in progress and the first results are presented in this paper.

Application of symbolic regression to the derivation of scaling laws for tokamak energy confinement time in terms of dimensionless quantities

In many scientific applications, it is important to investigate how certain properties scale with the parameters of the systems. The experimental studies of scalings have traditionally been addressed with log regression, which limits the results to power laws and to theoretical and not data-driven dimensionless quantities. This has also been the case in nuclear fusion, in which the scaling of the energy confinement time is a crucial aspect in understanding the physics of transport and in the design of future devices. Traditionally two main assumptions are at the basis of the most widely accepted empirical scaling laws for the confinement time: (a) the dimensionless variables used are the ones derived from the symmetries of the Vlasov equation; (b) the final scalings have the mathematical form of power laws. In this paper, it is shown how symbolic regression (SR), implemented with genetic programming (GP) techniques, can be used to test these hypotheses. Neither assumption is confirmed by the available data of the multi-machine International Tokamak Physics Activity (ITPA) of validated tokamak discharges. The statistically soundest expressions are not power laws and cannot be formulated in terms of the traditional dimensionless quantities. The consequences of the data-driven scaling laws obtained are both practical and theoretical: the confinement time for the ITER can be significantly shorter than foreseen by power laws and different dimensionless variables should be considered for theoretical investigations. On the other hand, higher quality databases should be built to reduce the uncertainties in the extrapolations. It is also worth emphasising that the proposed methodology is fully general and therefore can be applied to any field of science.

First tests of a multi-wavelength mini-DIAL system for the automatic detection of greenhouse gases

Considering the increase of atmospheric pollution levels in our cities, due to emissions from vehicles and domestic heating, and the growing threat of terrorism, it is necessary to develop instrumentation and gather know-how for the automatic detection and measurement of dangerous substances as quickly and far away as possible. The Multi- Wavelength DIAL, an extension of the conventional DIAL technique, is one of the most powerful remote sensing methods for the identification of multiple substances and seems to be a promising solution compared to existing alternatives. In this paper, first in-field tests of a smart and fully automated Multi-Wavelength mini-DIAL will be presented and discussed in details. The recently developed system, based on a long-wavelength infrared (IR-C) CO2 laser source, has the potential of giving an early warning, whenever something strange is found in the atmosphere, followed by identification and simultaneous concentration measurements of many chemical species, ranging from the most important Greenhouse Gases (GHG) to other harmful Volatile Organic Compounds (VOCs). Preliminary studies, regarding the fingerprint of the investigated substances, have been carried out by cross-referencing database of infrared (IR) spectra, obtained using in-cell measurements, and typical Mixing Ratios in the examined region, extrapolated from the literature. First experiments in atmosphere have been performed into a suburban and moderately-busy area of Rome. Moreover, to optimize the automatic identification of the harmful species to be recognized on the basis of in cell measurements of the absorption coefficient spectra, an advanced multivariate statistical method for classification has been developed and tested.

New analysis methods to push the boundaries of diagnostic techniques in the environmental sciences

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.

How to Handle Error Bars in Symbolic Regression for Data Mining in Scientific Applications

Symbolic regression via genetic programming has become a very useful tool for the exploration of large databases for scientific purposes. The technique allows testing hundreds of thousands of mathematical models to find the most adequate to describe the phenomenon under study, given the data available. In this paper, a major refinement is described, which allows handling the problem of the error bars. In particular, it is shown how the use of the geodesic distance on Gaussian manifolds as fitness function allows taking into account the uncertainties in the data, from the beginning of the data analysis process. To exemplify the importance of this development, the proposed methodological improvement has been applied to a set of synthetic data and the results have been compared with more traditional solutions.

On the Use of Transfer Entropy to Investigate the Time Horizon of Causal Influences between Signals

Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mutual information, have some significant limitations. More recently, transfer entropy has been proposed as a powerful tool to understand the flow of information between signals. In this paper, the comparative advantages of transfer entropy, for determining the time horizon of causal influence, are illustrated with the help of synthetic data. The technique has been specifically revised for the analysis of synchronization experiments. The investigation of experimental data from thermonuclear plasma diagnostics proves the potential and limitations of the developed approach.