Search

Aims: To formulate, implement, and validate a user-independent release of CLEAN for Fourier-based image reconstruction of hard X-rays flaring sources. Methods: CLEAN is an iterative deconvolution method for radio and hard X-ray solar imaging. In a specific step of its pipeline, CLEAN requires the convolution between an idealized version of the instrumental Point Spread Function (PSF), and a map collecting point sources located at positions on the solar disk from where most of the flaring radiation is emitted. This convolution step has highly heuristic motivations and the shape of the idealized PSF, which depends on the user's choice, impacts the shape of the overall reconstruction. Here we propose the use of an interpolation/extrapolation process to avoid this user-dependent step, and to realize a completely unbiased version of CLEAN. Results: Applications to observations recorded by the Spectrometer/Telescope for Imaging X-rays (STIX) on-board Solar Orbiter show that this unbiased release of CLEAN outperforms the standard version of the algorithm in terms of both automation and reconstruction reliability, with reconstructions whose accuracy is in line with the one offered by other imaging methods developed in the STIX framework. Conclusions: This unbiased version of CLEAN proposes a feasible solution to a well-known open issue concerning CLEAN, i.e., its low degree of automation. Further, this study provided the first application of an interpolation/extrapolation approach to image reconstruction from STIX experimental visibilities.

Aims. To provide a schematic mathematical description of the imaging concept of the Spectrometer/Telescope for Imaging X-rays (STIX) on board Solar Orbiter. The derived model is the fundamental starting point for both the interpretation of STIX data and the description of the data calibration process. Methods. We describe the STIX indirect imaging technique which is based on spatial modulation of the X-ray photon flux by means of tungsten grids. We show that each of 30 STIX imaging sub-collimators measures a complex Fourier component of the flaring X-ray source corresponding to a specific angular frequency. We also provide details about the count distribution model, which describes the relationship between the photon flux and the measured pixel counts. Results. We define the image reconstruction problem for STIX from both visibilities and photon counts. We provide an overview of the algorithms implemented for the solution of the imaging problem, and a comparison of the results obtained with these different methods in the case of the SOL2022-03-31T18 flaring event.

Context. The Spectrometer and Telescope for Imaging X-rays (STIX) on board Solar Orbiter observes solar X-ray emission in the range of 4- 150 keV and produces spectra and images of solar flares over a wide range of flare magnitudes. During nominal operation, STIX continuously generates data. A constant data flow requires fully automated data-processing pipelines to process and analyze the data, and a data platform to manage, visualize, and distribute the data products to the scientific community. Aims. The STIX Data Center has been built to fulfill these needs. In this paper, we outline its main components to help the community better understand the tools and data it provides. Methods. The STIX Data Center is operated at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW) and consists of automated processing pipelines and a data platform. The pipelines process STIX telemetry data, perform common analysis tasks, and generate data products at different processing levels. They have been designed to operate fully automatically with minimal human intervention. The data platform provides web-based user interfaces and application programmable interfaces for searching and downloading STIX data products. Results. The STIX Data Center has been operating successfully for more than two years. The platform facilitates instrument operations and provides vital support to STIX data users., Astronomy & Astrophysics, 673, ISSN:0004-6361, ISSN:1432-0746

The design of conical frustum phased array antennas for air traffic control (ATC) radar systems is addressed. The array architecture, which is controlled by a fully digital beam-forming (DBF) network, is composed by a set of equal vertical modules. Each module consists of a linear sparse array that generates on receive multiple instantaneous beams pointing along different directions in elevation. To reach the best trade-off between the antenna complexity (i.e., minimum number of array elements and/or radio frequency components) and radiation performance (i.e., matching a set of reference patterns), the synthesis problem is formulated in the Compressive Sampling (CS) framework. Then, the positions of the array elements and the complex excitations for generating each single beam are jointly determined through a customized version of the Bayesian CS (BCS) tool. Representative numerical results, concerned with ideal as well as real antenna models, are reported both to validate the proposed design strategy and to assess the effectiveness of the synthesized modular sparse array architecture also in comparison with conventional arrays with uniformly-spaced elements.

The Spectrometer/Telescope for Imaging X-rays (STIX) is the instrument of the Solar Orbiter mission conceived for the observation of the X-ray radiation emitted during solar flares. STIX adopts an indirect imaging technique based on the use of modulation collimators, i.e., pairs of grids mounted in front of detectors. By measuring the modulated X-ray photon flux, the telescope provides the complex values of 30 Fourier components of the angular distribution of the X-ray source, named visibilities. Hence, the imaging problem for STIX is the inverse problem of reconstructing the image of the flaring X-ray source from a sparse sampling of its Fourier transform. In this thesis, we describe the STIX imaging concept and the image reconstruction problem both from visibilities and from photon count measurements. We present different image reconstruction techniques: the count-based Expectation Maximization (EM) algorithm, the visibility-based maximum entropy method MEM_GE and a neural network approach for parametric image reconstruction. We test these methods on synthetic STIX data and, in the case of MEM_GE, on real data provided by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). We propose a stopping rule for EM based on a predictive risk estimator. This estimator is derived by using a Poisson counterpart of Stein?s lemma for Gaussian variates. We validate the performance of the stopping rule in the case of the STIX imaging problem from synthetic data and in the case of a deconvolution inverse problem. Finally, we present the results of the STIX imaging problem from real data. We describe the first approaches of image reconstruction from semi-calibrated data, i.e., visibility amplitudes only. Then, we provide an overview of the visibility phase calibration, and we show the reconstructions from fully-calibrated visibilities obtained by using several methods, including MEM_GE and EM. The STIX reconstructions are validated by comparison with maps of the same events provided by the Atmospheric Imaging Assembly on board the Solar Dynamic Observatory (SDO/AIA) in UV wavelengths.

In this work we deal with parametric inverse problems, which consist in recovering a finite number of parameters describing the structure of an unknown object, from indirect measurements. State-of-the-art methods for approximating a regularizing inverse operator by using a dataset of input-output pairs of the forward model rely on deep learning techniques. In these approaches, a neural network is trained to predict the value of the sought parameters directly from the data. In this paper, we show that these methods provide suboptimal results when the topology of the parameter space is strictly coarser than the Euclidean one. To overcome this issue, we propose a two-step strategy for approximating a regularizing inverse operator by means of a neural network, which works under general topological conditions. First, we embed the parameters into a subspace of a low-dimensional Euclidean space; second, we use a neural network to approximate a homeomorphism between the subspace and the image of the parameter space through the forward operator. The parameters are then retrieved by applying the inverse of the embedding to the network predictions. The results are shown for the problem of X-ray imaging of solar flares with data from the Spectrometer/Telescope for Imaging X-rays. In this case, the parameter space is a family of Moebius strips that collapse into a point. Our simulation studies show that the use of a neural network for predicting the parameters directly from the data yields systematic errors due to the non-Euclidean topology of the parameter space. The proposed strategy overcomes the topological issues and furnishes stable and accurate reconstructions.

Given that little is known about regional language user interaction practices on Wikipedia, this study analyzed content creation process, user social interaction and exchanged content over the course of the existence of Venetian Wikipedia. Content of and user interactions over time on Venetian Wikipedia exhibit practices shared within larger Wikipedia communities and display behaviors that are pertinent to this specific community. Shared practices with other Wikipedias (eg. English Wikipedia) included coordination content as a dominant category of exchanged content, user-role based structure where and most active communicators are administrators was another shared feature, as well as socialization tactics to involve users in online projects. While Venetian Wikipedia stood out for its geographically-linked users who emphasized their regional identity. User exchanges over time spilled over from online to offline domains. This analysis provides a different side of Wikipedia collaboration which is based on creation, maintenance, and negotiation of the content but also shows engagement into interpersonal communication. Thus, this study exemplifies how regional language Wikipedias provide ways to their users not only to preserve their cultural heritage through the language use on regional language Wikipedia space and connect through shared contents of interest, but also, how it could serve as a community maintenance platform that unifies users with shared goals and extends communication to offline realm.

Background: Diagnostic and therapeutic advances have led to much greater awareness of transthyretin cardiac amyloidosis (ATTR-CA). We aimed to characterize changes in the clinical phenotype of patients diagnosed with ATTR-CA over the past 20 years. Methods: This is a retrospective observational cohort study of all patients referred to the National Amyloidosis Centre (2002–2021) in whom ATTR-CA was a differential diagnosis. Results: We identified 2995 patients referred with suspected ATTR-CA, of whom 1967 had a diagnosis of ATTR-CA confirmed. Analysis by 5-year periods revealed an incremental increase in referrals, with higher proportions of patients having been referred after bone scintigraphy and cardiac magnetic resonance imaging (2% versus 34% versus 51% versus 55%, chi-square P P P P P =0.01) and higher left ventricular ejection fraction (46.0%±8.9% versus 46.8%±11.0% versus 47.8%±11.0% versus 49.5%±11.1%, P P P P Conclusions: There has been a substantial increase in ATTR-CA diagnoses, with more patients being referred after local advanced cardiac imaging. Patients are now more often diagnosed at an earlier stage of the disease, with substantially lower mortality. These changes may have important implications for initiation and outcome of therapy and urgently need to be factored into clinical trial design.

Collective memories are precious resources for the society, because they contribute to strengthening the emotional bonding between community members, maintaining groups cohesion, and directing future behavior. Understanding the formation of the collective memories of emotional upheavals is important to a better comprehension of people's reactions and of the consequences on their psychological health. Previous studies investigated the effects of single traumatizing events, but few of them applied a quantitative approach to analyze the different psychological processes associated to the collective memories formation of upheavals on a large scale. This thesis explores the opportunities of applying quantitative methods to the study of collective memories in a collaborative environment such as the English Wikipedia. First, the presence of commemoration processes in Wikipedia articles and talk pages about traumatic events will be investigated through the analysis of edit activity patterns. Second, natural language processing techniques will be applied to detect differences in the collective representations of traumatic and non traumatic events, in the temporal focus of old and recent traumatic events, and in the representations of natural and human-made disasters. Third, the temporal evolution of language related to emotional, cognitive and social processes will be analyzed in the talk pages of two different emotional upheavals, the 2005 London bombings and the 2011 Egyptian revolution. The results will confirm the interpretation of Wikipedia as a global memory place, and highlight specific psychological processes related to the formation of collective memories of different types of traumatic events, opening the way to the quantitative study of collective memory formation in digital collaborative environments.