Solid state effects in electron emission from atomic collisions near surfaces

We present a brief progress report of recent studies of the ejected electron spectra arising from glancing-angle ion-surface scattering involving collision energies of hundreds of keV/u. A broad range of electron energies and emission angles is analyzed containing prominent structures such as the convoy electron peak and the binary ridge. Particular emphasis is placed on the search for signatures of dynamic image interactions and multiple scattering near surfaces. 30 refs., 8 figs

Semiclassical interferences and catastrophes in the ionization of Rydberg atoms by half-cycle pulses

A multi-dimensional semiclassical description of excitation of a Rydberg electron by half-cycle pulses is developed and applied to the study of energy- and angle-resolved ionization spectra. Characteristic novel phenomena observable in these spectra such as interference oscillations and semiclassical glory and rainbow scattering are discussed and related to the underlying classical dynamics of the Rydberg electron. Modifications to the predictions of the impulse approximation are examined that arise due to finite pulse durations

On the unexpected oscillation of the total cross section for excitation in He{sup 2+} + H collisions

Recent calculations and measurements have revealed unexpected oscillations of the total cross section for excitation in low- to intermediate-energy He{sup 2+} + H collisions. A physical explanation of this behavior is given here stemming from analysis of classical trajectory Monte Carlo simulations, molecular orbital close coupling calculations, and solution of the time-dependent Schroedinger equation on a numerical lattice. These results indicate that the observed behavior should be characteristic of a wide range of reactions in ion-atom collisions

Electronic excitation in transmission of relativistic H{sup {minus}} ions through thin foils

The authors describe a theoretical model to study the transmission of relativistic H{sup {minus}} ions through thin carbon foils. The approach is based on a Monte Carlo solution of the Langevin equation describing electronic excitations of the atoms during the transport through the foil. Calculations for the subshell populations of outgoing hydrogen atoms are found to be in good agreement with recent experimental data on an absolute scale and show that there exists a propensity for populating extreme Stark states

Enhanced population of high-l states due to the interplay between multiple scattering and dynamical screening in ion-solid collisions

We present a study of the sub-shell populations of 13.6 MeV/u Ar17+ ions after transmission through thin carbon foils. We show that the combined effect of the wake field induced by the ion in the solid and multiple collisions leads to a strongly enhanced population of high angular momentum states. These results explain new experimental data for absolute total line emission intensities

Quantum transport of the internal state of Kr35+ ions through amorphous carbon foils

The population dynamics of the internal state of 60 MeV/u Kr35+ ions traversing amorphous carbon foils is studied theoretically and experimentally. This system is of particular interest as the times scales for collisional distribution, mixing due to the wake field, and radiative processes are comparable to each other. A transport theory based on a quantum-trajectory Monte Carlo method is developed, which treats the collisional and radiative redistribution of states on the same footing. The simulations exhibit clear signatures for the interplay between radiative decay and collisional mixing. Good agreement with experimental data is found

Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies

International audienceMulticenter studies are needed to demonstrate the clinical potential value of radiomics as a prognostic tool. However, variability in scanner models, acquisition protocols and reconstruction settings are unavoidable and radiomic features are notoriously sensitive to these factors, which hinders pooling them in a statistical analysis. A statistical harmonization method called ComBat was developed to deal with the "batch effect" in gene expression microarray data and was used in radiomics studies to deal with the "center-effect". Our goal was to evaluate modifications in ComBat allowing for more flexibility in choosing a reference and improving robustness of the estimation. Two modified ComBat versions were evaluated M-ComBat allows to transform all features distributions to a chosen reference, instead of the overall mean, providing more flexibility. B-ComBat adds bootstrap and Monte Carlo for improved robustness in the estimation. BM-ComBat combines both modifications. The four versions were compared regarding their ability to harmonize features in a multicenter context in two different clinical datasets. The first contains 119 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging and positron emission tomography imaging. In that case ComBat was applied with 3 labels corresponding to each center. The second one contains 98 locally advanced laryngeal cancer patients from 5 centers with contrast-enhanced computed tomography. In that specific case, because imaging settings were highly heterogeneous even within each of the five centers, unsupervised clustering was used to determine two labels for applying ComBat. The impact of each harmonization was evaluated through three different machine learning pipelines for the modelling step in predicting the clinical outcomes, across two performance metrics (balanced accuracy and Matthews correlation coefficient). Before harmonization, almost all radiomic features had significantly different distributions between labels. These differences were successfully removed with all ComBat versions. The predictive ability of the radiomic models was always improved with harmonization and the improved ComBat provided the best results. This was observed consistently in both datasets, through all machine learning pipelines and performance metrics. The proposed modifications allow for more flexibility and robustness in the estimation. They also slightly but consistently improve the predictive power of resulting radiomic models