Studio dell'ottica di un fascio di ioni negativi e sua interazione con il gas di fondo

L’esperimento NIO1, in funzione presso il Consorzio RFX di Padova, ha lo scopo di studiare e determinare le migliori condizioni di funzionamento per una sorgente di fasci di ioni negativi, del tipo impiegato per il prototipo di reattore a fusione nucleare del progetto ITER. In sistemi di questo tipo i fasci di ioni negativi vengono estratti e accelerati ad alte energie per mezzo di una serie di griglie di accelerazione poste a potenziale crescente; per descrivere il comportamento del fascio estratto sono tipicamente impiegati dei codici numerici che permettono di avere informazioni preliminari sull’esperimento. In questo lavoro di tesi, partendo da un codice basato sul pacchetto di simulazione IBSimu e ottimizzato in precedenza per descrivere il solo fascio primario di ioni negativi estratto nell’esperimento NIO1, si è andati a individuare e studiare i principali processi di produzione di particelle secondarie che possono avvenire nell’acceleratore, per esempio per interazione del fascio con il gas di fondo o con le griglie di accelerazione; si sono quindi implementati tali processi nel codice al fine di poter effettuare delle simulazioni quanto più vicine alla realtà e ottenere risultati in migliore accordo con quelli sperimentali. In un’ulteriore parte del lavoro ci si è focalizzati sull’ottimizzazione del codice attraverso uno studio dei parametri numerici, al fine di ottenere risultati stabili e affidabili, e sulla sua validazione mediante un confronto con i risultati forniti dal codice numerico EAMCC, ampiamente utilizzato in diversi laboratori; si è infine passati a un confronto con i reali risultati sperimentali di NIO1.ope

Topicalized PPs: Movement or External Merge?

Contribution to Linguistic Evidence 202

From Chirps to Random-FM Excitations in Pulse Compression Ultrasound Systems

Pulse compression is often practiced in ultrasound Non Destructive Testing (NDT) systems using chirps. However, chirps are inadequate for setups where multiple probes need to operate concurrently in Multiple Input Multiple Output (MIMO) arrangements. Conversely, many coded excitation systems designed for MIMO miss some chirp advantages (constant envelope excitation, easiness of bandwidth control, etc.) and may not be easily implemented on hardware originally conceived for chirp excitations. Here, we propose a system based on random-FM excitations, capable of enabling MIMO with minimal changes with respect to a chirp-based setup. Following recent results, we show that random-FM excitations retain many advantages of chirps and provide the ability to frequency-shape the excitations matching the transducers features.Comment: 4 pages, 4 figures. Post-print from conference proceedings. Note that paper in conference proceedings at http://dx.doi.org/10.1109/ULTSYM.2012.0117 has some rendering issue

Synergy of Cassini SAR and altimeter acquisitions for the retrieval of dune field characteristics on Titan

This work focuses on the retrieval of Titan’s dune field characteristics addressing different radar modes. The main purpose of the proposed work is to exploit a possible synergy between SAR and altimeter acquisitions modes to provide information about dune field. Cassini has performed 86 Titan flybys in which several observations of dune fields have been collected in altimetry mode. There are several cases in which SAR and altimeter have been acquired over same areas covered by dune fields, such as during T28 (SAR) and T30 (altimeter) flybys. Altimetry together with SAR data have been used to derive the rms slopes of dunes (large scale) over Fensal area, this information has been employed to calculate SAR incidence angle with respect to dunes. We extracted backscattering coefficients of bright and dark areas detected in the analyzed SAR image in order to evaluate the angular response of scattering. Through the Geometric Optics model we retrieve roughness values (small scale rms slope) for both dune bright and dark areas

Metadynamics for perspective drug design: Computationally driven synthesis of new protein-protein interaction inhibitors targeting the EphA2 receptor

Metadynamics (META-D) is emerging as a powerful method for the computation of the multidimensional freeenergy surface (FES) describing the protein-ligand binding process. Herein, the FES of unbinding of the antagonist N-(3α-hydroxy-5β-cholan-24-oyl)-L-β-homotryptophan (UniPR129) from its EphA2 receptor was reconstructed by META-D simulations. The characterization of the free-energy minima identified on this FES proposes a binding mode fully consistent with previously reported and new structure-activity relationship data. To validate this binding mode, new N-(3α-hydroxy-5β-cholan-24-oyl)-L-β-homotryptophan derivatives were designed, synthesized, and tested for their ability to displace ephrin-A1 from the EphA2 receptor. Among them, two antagonists, namely compounds 21 and 22, displayed high affinity versus the EphA2 receptor and resulted endowed with better physicochemical and pharmacokinetic properties than the parent compound. These findings highlight the importance of free-energy calculations in drug design, confirming that META-D simulations can be used to successfully design novel bioactive compounds

Three-Dimensional Shapes of Spinning Helium Nanodroplets

A significant fraction of superfluid helium nanodroplets produced in a free-jet expansion have been observed to gain high angular momentum resulting in large centrifugal deformation. We measured single-shot diffraction patterns of individual rotating helium nanodroplets up to large scattering angles using intense extreme ultraviolet light pulses from the FERMI free-electron laser. Distinct asymmetric features in the wide-angle diffraction patterns enable the unique and systematic identification of the three-dimensional droplet shapes. The analysis of a large dataset allows us to follow the evolution from axisymmetric oblate to triaxial prolate and two-lobed droplets. We find that the shapes of spinning superfluid helium droplets exhibit the same stages as classical rotating droplets while the previously reported metastable, oblate shapes of quantum droplets are not observed. Our three-dimensional analysis represents a valuable landmark for clarifying the interrelation between morphology and superfluidity on the nanometer scale

Combined Use of Sentinel-1 and Sentinel-2 for Glacier Mapping: An Application Over Central East Alps

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Deep neural networks for classifying complex features in diffraction images

Intense short-wavelength pulses from free-electron lasers and high-harmonic-generation sources enable diffractive imaging of individual nano-sized objects with a single x-ray laser shot. The enormous data sets with up to several million diffraction patterns represent a severe problem for data analysis, due to the high dimensionality of imaging data. Feature recognition and selection is a crucial step to reduce the dimensionality. Usually, custom-made algorithms are developed at a considerable effort to approximate the particular features connected to an individual specimen, but facing different experimental conditions, these approaches do not generalize well. On the other hand, deep neural networks are the principal instrument for today's revolution in automated image recognition, a development that has not been adapted to its full potential for data analysis in science. We recently published in Langbehn et al. (Phys. Rev. Lett. 121, 255301 (2018)) the first application of a deep neural network as a feature extractor for wide-angle diffraction images of helium nanodroplets. Here we present the setup, our modifications and the training process of the deep neural network for diffraction image classification and its systematic benchmarking. We find that deep neural networks significantly outperform previous attempts for sorting and classifying complex diffraction patterns and are a significant improvement for the much-needed assistance during post-processing of large amounts of experimental coherent diffraction imaging data.Comment: Published Version. Github code available at: https://github.com/julian-carpenter/airyne