Spectra of Coherent Smith-Purcell Radiation Observed from Short Electron Bunches: Numerical and Experimental Studies

MOPWA056 - ISBN978-3-95450-122-9International audienceThere is a significant interest in the development of compact particle accelerators within research areas including X-ray and THz (T-ray) sources of radiation, particle physics and medical sciences. To support the progress in these areas, non-invasive, electron beam diagnostics that are capable of measuring a single femtosecond electron bunch are required. At the current stage such beam diagnostics for femtosecond-long electron bunches are still not available. The goal of the work presented is to understand the spectral characteristics of coherent Smith-Purcell radiation to enable its quick and reliable interpretation including the longitudinal profile reconstruction of electron bunches. The research presented comprises results from numerical modelling and experimental studies. Using the numerical data, we discuss the radiated spectra dependence on the electron bunch profile and analyse the results. We also discuss the experimental data and compare it with theoretical predictions

Spectra of coherent Smith-Purcell radiation observed from short electron bunches: numerical and experimental studies

To stimulate and support the progress in areas of compact particle accelerators, within research fields including X-ray and THz (T-ray) sources of radiation, non-invasive, electron beam diagnostics that are capable of measuring a single femtosecond electron bunch are required. At the current stage such beam diagnostics for femtosecond-long electron bunches are still not available. The goal of the work presented is to understand the spectral characteristics of coherent Smith-Purcell radiation to enable its quick and reliable interpretation including the longitudinal profile reconstruction of electron bunches. The research presented comprises results from numerical modelling and experimental studies. We use a PiC numerical model to analyse the dependence of radiated spectra dependence on the electron bunch profile, and discuss our results in the context of new experimental data from the E203 experiment at SLAC. Copyright © 2013 by JACoW

Prediction of quantiles by statistical learning and application to GDP forecasting

International audienceIn this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator (also known as Exponentially Weighted aggregate) is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of Koenker and Bassett (1978), this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results