1,721,048 research outputs found
Feature augmentation for the inversion of the Fourier transform with limited data
Full text linkWe investigate an interpolation/extrapolation method that, given scattered observations of the Fourier transform, approximates its inverse. The interpolation algorithm takes advantage of modeling the available data via a shape-driven interpolation based on variably scaled Kernels (VSKs), whose implementation is here tailored for inverse problems. The so-constructed interpolants are used as inputs for a standard iterative inversion scheme. After providing theoretical results concerning the spectrum of the VSK collocation matrix, we test the method on astrophysical imaging benchmarks
Compressed sensing and Sequential Monte Carlo for solar hard X-ray imaging
Full text linkWe describe two inversion methods for the reconstruction of hard Xray solar images. The methods are tested against experimental visibilities recorded by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) and synthetic visibilities based on the design of the Spectrometer/Telescope for Imaging X-rays (STIX)
Interactive graphical system for segmentation of multimodal medical volumes using fuzzy clustering
No full textAn interpolation/extrapolation approach to X-ray imaging of solar flares
No full textWe describe an interpolation/extrapolation procedure that recon- structs X-ray maps of solar flares using as input data sparse samples of the Fourier transform of the radiation flux, named visibilities. The algorithm is based on two steps: in the first step the performance of an interpolation rou- tine is optimized by representing the visibilities according to favorable coor- dinates in the frequency plane. In the second step two extrapolation schemes are introduced, respectively based on the projection and the thresholding of the Landweber iterative method. The procedure is validated against realistic synthetic visibilities and applied to experimental measurements provided by the NASA satellite Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
No full textSolar active regions can significantly disrupt the Sun-Earth space environment, leading to severe space weather events such as solar flares or coronal mass ejections. Consequently, the automatic classification of active region groups is a crucial starting point for accurately and promptly predicting solar activity. This study presents our application of deep learning techniques to classify active region cutouts based on the Mount Wilson classification scheme. We explore the latest advantages in image classification architectures, ranging from convolutional neural networks to vision transformers, alongside modern training procedures, including on-the-fly data augmentations and transfer learning. We aim at evaluating the respective strengths and limitations of different neural network architectures in classifying solar active region cutouts. We observed that combining magnetogram and continuum image types enhances model performance by leveraging complementary features from diverse inputs. When considering only magnetograms, data-efficient image transformers achieve the best performance, suggesting that these models can better capture the spatial complexity of magnetograms. Models trained exclusively on continuum images exhibit overall lower performance, suggesting that continuum images, due to their more homogeneous nature, offer less spatial variability
Compressed sensing and finite isotropic wavelets for the STIX reconstruction problem
No full textSTIX is a X-ray imaging spectroscopy device to be mounted as part of the Solar Orbiter cluster. Its goal is to provide images and spectra of solar flaring regions. The device provides 30 measurements of the incoming photon flux which can be interpreted as spatial Fourier samples. In this paper we present a method for reconstructing the intensity image from the few provided measurements. The proposed algorithm is based on compressed sensing theory. In order to provide the needed sparsity, we build an isotropic wavelet transform which is very appropriate for the STIX measurements. The evaluation on two simulated solar flares shows the potential of the algorithm in reconstructing hard X-ray maps from STIX measurements
Geometry of the Hough Transforms with Applications to Synthetic Data
No full textIn the framework of the Hough transform technique to detect curves in images, we provide a bound for the number of Hough transforms to be considered for a successful optimization of the accumulator function in the recognition algorithm. Such a bound is consequence of geometrical arguments. We also show the robustness of the results when applied to synthetic datasets strongly perturbed by noise. An algebraic approach, discussed in the appendix, leads to a better bound of theoretical interest in the exact case
Artificial Intelligence for the Characterization of the 2024 May Superstorm: Active Region Classification, Flare Forecasting, and Geomagnetic Storm Prediction
No full textSpace weather, driven by solar flares and coronal mass ejections, poses significant risks to technological systems. Accurately forecasting these events and their impact on Earth’s magnetosphere remains a challenge because of the complexity of solar-terrestrial interactions. This study focuses on the solar and geomagnetic extreme events associated with the 2024 May superstorm and shows that artificial intelligence (AI) tools are able to characterize this storm at three different levels. First, using magnetogram cutouts, a vision transformer was able to classify the morphological evolution of NOAA Active Region 13644 (primarily involved in storm generation), and then a video-based deep learning method predicted the occurrence of the associated solar flares, and a data-driven method exploited in situ measurements to raise 1-hour in advance alerts of the geomagnetic storm during the entire event. These AI models outperformed traditional methods in predicting solar flare occurrences, onset, and recovery phases of the geomagnetic storm. These findings highlight the impressive potential of AI for space weather forecasting as a tool to mitigate the impact of extreme solar events on critical infrastructure
Machine Learning as a Flaring Storm Warning Machine: Was a Warning Machine for the 2017 September Solar Flaring Storm Possible?
No full textMachine learning is now one of the methodologies of choice for flare forecasting, and supervised techniques, in both their traditional and deep versions, are becoming more frequently used for prediction in this area of space weather. Most studies assess the prediction effectiveness of machine-learning methods by computing confusion matrices, which are typically highly non-diagonal, particularly in applications concerning the forecasting of X-class flares. The present study suggests that the reliability of the outcomes of a supervised machine-learning method could be better assessed by using it as a warning machine, sounding binary alerts unrolled over time, and by comparing the number of alerts sounded by the machine in specific time windows with the number of events actually observed in those time windows. Indeed, when applied to the prediction of the events associated with the 2017 September solar storm, a hybrid LASSO algorithm was able to sound alerts every day a flare actually occurred; it also identified the corresponding flare class. In addition, the machine was able to predict with some accuracy a reliable proxy of the energy budget daily released by magnetic reconnection during the entire course of the storm. Finally, the analysis shows that the combination of sparsity-enhancing machine learning and feature ranking could allow the identification of the prominent role that the Ising energy played as an active region property in the forecasting process
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