Vincent Mazet

Dynamics of highly excited barium atoms deposited on large argon clusters. I. General trends

Ba(Ar)(approximately 750) clusters were generated by associating the supersonic expansion and the pick-up techniques. A femtosecond pump (266.3 nm)-probe (792 or 399.2 nm) experiment was performed to document the dynamics of electronically excited barium within the very multidimensional environment of the argon cluster. Barium was excited in the vicinity of the 6s9p (1)P state and probed by ionization. The velocity imaging technique was used to monitor the energy distribution of photoelectrons and photoions as a function of the delay time between the pump and the probe pulses. A complex dynamics was revealed, which can be interpreted as a sequence/superposition of elementary processes, one of which is the ejection of barium out of the cluster. The latter has an efficiency, which starts increasing 5 ps after the pump pulse, the largest ejection probability being at 10 ps. The ejection process lasts at a very long time, up to 60 ps. A competing process is the partial solvation of barium in low lying electronic states. Both processes are preceded by a complex electronic relaxation, which is not fully unraveled here, the present paper being the first one in a series.

Simulation of postive normal variables using several proposal distributions

In this paper, we propose a new methodology to generate random variables distributed according to a Gaussian with positive support. We narrow the study to the univariate case. The method consists in an accept-reject algorithm in which a previous step is added consisting in choosing among several proposal distributions the one which gives the highest average probability of acceptance for given parameters of the target distribution. This results in a very fast method since it generates low reject

Time resolved observation of the solvation dynamics of a Rydberg excited molecule deposited on an argon cluster-I: DABCO☆ at short times

This paper is a joint experimental and theoretical approach concerning a molecule deposited on a large argon cluster. The spectroscopy and the dynamics of the deposited molecule are measured using the photoelectron spectroscopy. The absorption spectrum of the deposited molecule shows two solvation sites populated in the ground state. The combined dynamics reveals that the population ratio of the two sites is reversed when the molecule is electronically excited. This work provides the timescale of the corresponding solvation dynamics. Theoretical calculation supports the interpretation. More generally, close examination of the short time dynamics (0-6 ps) of DABCO···Ar(n) gives insights into the ultrafast relaxation dynamics of molecules deposited at interfaces and provides hence the time scale for deposited molecules to adapt to their neighborhoods.

Joint Bayesian Decomposition of a Spectroscopic Signal Sequence

This letter addresses the problem of decomposing a sequence of spectroscopic signals: data are a series of (energy or electromagnetic) spectra and we aim to estimate the peak parameters (centers, amplitudes, and widths). The key idea is to perform the decomposition of the whole sequence and to impose the parameters to evolve smoothly through the sequence. The problem is set within a Bayesian framework whose posterior distribution is sampled using a Markov chain Monte Carlo simulated annealing algorithm. Simulations conducted on synthetic data illustrate the performance of the method.

Automatic rectangular building detection from VHR aerial imagery using shadow and image segmentation

This paper introduces a novel approach for the automated detection of rectangular buildings from monocular very high resolution (VHR) aerial images. The overall idea of this work is first to decompose the image into small homogeneous regions and treat all regions as candidates. According to the position of the shadows, a merging process is then performed over regions having similar spectral characteristics to produce building regions whose shapes are appropriate to rectangles. The experimental results prove that the proposed method is applicable in various areas (high dense urban, suburban, and rural) and is highly robust and reliable.

Unsupervised joint decomposition of a spectroscopic signal sequence

This paper addresses the problem of decomposing a sequence of spectroscopic signals. Data are a series of signals modeled as a noisy sum of parametric peaks. We aim to estimate the peak parameters given that they change slowly between two contiguous signals. The key idea is to decompose the whole sequence rather than each signal independently. The problem is set within a Bayesian framework. The peaks with similar evolution are gathered into groups and a Markovian prior on the peak parameters of a same group is used to favor a smooth evolution of the peaks. In addition, the peak number and the group number are unknown and have to be estimated (the number of peaks in two contiguous signals change if peaks vanish). Therefore, the posterior distribution is sampled with a reversible jump Markov chain Monte Carlo algorithm. Simulations conducted on synthetic and real photoelectron data illustrate the performance of the method. HighlightsThis work aims at estimating the parameters of Gaussian peaks in spectroscopic signals.Data gather actually several spectroscopic signals, so the decomposition is performed jointly on the whole data.The peaks evolve slowly through the data and may appear and disappear.We propose an original Bayesian model and an implementation of the RJMCMC algorithm.The performances of the method are discussed on synthetic and real (photoelectron) data.

Sparse spike train deconvolution using the hunt filter and a thresholding method

A new deconvolution method of sparse spike trains is presented. It is based on the coupling of the Hunt filter with a thresholding. We show that a good model for the probability density function of the Hunt filter output is a Gaussian mixture, from which we derive the threshold that minimizes the probability of errors. Based on an interpretation of the method as a maximum a posteriori (MAP) estimator, the hyperparameters are estimated using a joint MAP approach. Simulations show that this method performs well at a very low computation time.

Hierarchical multispectral galaxy decomposition using a MCMC algorithm with multiple temperature simulated annealing

We present a new method for the parametric decomposition of barred spiral galaxies in multispectral observations. The observation is modelled with a realistic image formation model and the galaxy is composed of physically significant parametric structures. The model also includes a parametric filtering to remove non-desirable aspects of the observation. Both the model and the filter parameters are estimated by a robust Monte Carlo Markov chain (MCMC) algorithm. The algorithm is based on a Gibbs sampler combined with a novel strategy of simulated annealing in which several temperatures allow to manage efficiently the simulation effort. Besides, the overall decomposition is performed following an original framework: a hierarchy of models from a coarse model to the finest one is defined. At each step of the hierarchy the estimate of a coarse model is used to initialize the estimation of the finer model. This leads to an unsupervised decomposition scheme with a reduced computation time. We have validated the method on simulated and real 5-band images: the results showed the accuracy and the robustness of the proposed approach.

Detection of faint extended sources in hyperspectral data and application to HDF-S MUSE observations

Circum-Galactic Medium surrounding galaxies has been punctually detected, but its morphology remains largely unknown. The Multi-Unit Spectroscopic Explorer (MUSE) spectro-imager provides for the first time both spectral and spatial resolution to spatially map such features. The problem lies in the statistical detection of faint spatially-extended sources in massive hyperspectral images such as provided by MUSE, and has not been previously handled. This paper presents a statistical detection method based on hypothesis testing tackling this problem. The proposed strategy is step-by-step validated over alternative ways with simulations. Then, results on MUSE observations are presented.

MRF and Dempster-Shafer theory for simultaneous shadow/vegetation detection on high resolution aerial color images

This paper presents a new method for simultaneously detecting shadows and vegetation in remote sensing images, based on Otsus thresholding method and Dempster-Shafer (DS) fusion which aims at combining different shadow indices and vegetation indices in order to increase the information quality and to obtain a more reliable and accurate segmentation result. The DS fusion is carried out pixel by pixel and is incorporated in the Markovian context while obtaining the optimal segmentation with the energy minimization scheme associated with the MRF. This new approach is applied on remote sensing images and demonstrates its efficiency.