A hybrid supervised/unsupervised machine learning approach to solar flare prediction

We introduce a hybrid approach to solar flare prediction, whereby a supervised regularization method is used to realize feature importance and an unsupervised clustering method is used to realize the binary flare/no-flare decision. The approach is validated against NOAA SWPC data

Properties of the Acceleration Regions in Several Loop-structured Solar Flares

Using {\em RHESSI} hard X-ray imaging spectroscopy observations, we analyze electron flux maps for a number of extended coronal loop flares. For each event, we fit a collisional model with an extended acceleration region to the observed variation of loop length with electron energy $E$, resulting in estimates of the plasma density in, and longitudinal extent of, the acceleration region. These quantities in turn allow inference of the number of particles within the acceleration region and hence the filling factor $f$ -- the ratio of the emitting volume to the volume that encompasses the emitting region(s). We obtain values of $f$ that lie mostly between 0.1 and 1.0; the (geometric) mean value is $f = 0.20 \times \div 3.9$, somewhat less than, but nevertheless consistent with, unity. Further, coupling information on the number of particles in the acceleration region with information on the total rate of acceleration of particles above a certain reference energy (obtained from spatially-integrated hard X-ray data) also allows inference of the specific acceleration rate (electron s$^{-1}$ per ambient electron above the chosen reference energy). We obtain a (geometric) mean value of the specific acceleration rate $\eta(20$ keV) $= (6.0 \times / \div 3.4) \times 10^{-3}$ electrons s$^{-1}$ per ambient electron; this value has implications both for the global electrodynamics associated with replenishment of the acceleration region and for the nature of the particle acceleration process

Solar hard X-ray imaging by means of Compressed Sensing and Finite Isotropic Wavelet Transform

This paper shows that compressed sensing realized by means of regularized deconvolution and the Finite Isotropic Wavelet Transform is effective and reliable in hard X-ray solar imaging. The method utilizes the Finite Isotropic Wavelet Transform with Meyer function as the mother wavelet. Further, compressed sensing is realized by optimizing a sparsity-promoting regularized objective function by means of the Fast Iterative Shrinkage-Thresholding Algorithm. Eventually, the regularization parameter is selected by means of the Miller criterion. The method is applied against both synthetic data mimicking the Spectrometer/Telescope Imaging X-rays (STIX) measurements and experimental observations provided by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). The performances of the method are compared with the results provided by standard visibility-based reconstruction methods. The results show that the application of the sparsity constraint and the use of a continuous, isotropic framework for the wavelet transform provide a notable spatial accuracy and significantly reduce the ringing effects due to the instrument point spread functions

Expectation Maximization for Hard X-ray Count Modulation Profiles

This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI)} instrument. Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized for the analysis of count modulation profiles in solar hard X-ray imaging based on Rotating Modulation Collimators. The algorithm described in this paper solves the maximum likelihood problem iteratively and encoding a positivity constraint into the iterative optimization scheme. The result is therefore a classical Expectation Maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of Expectation Maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. If optimally stopped, Expectation Maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data

Hunter-gatherers, biogeographic barriers and the development of human settlement in Tierra del Fuego

Tierra del Fuego represents the southernmost limit of human settlement in the Americas. While people may have started to arrive there around 10 500 BP, when it was still connected to the mainland, the main wave of occupation occurred 5000 years later, by which time it had become an island. The co-existence in the area of maritime hunter-gatherers(in canoes) with previous terrestrial occupants pre-echoes the culturally distinctive groups encountered by the first European visitors in the sixteenth century. The study also provides a striking example of interaction across challenging natural barriers

La Quintrala, protagonista vigilada

Indexación: Revista UNA

Felipe Guamán Poma de Ayala y su doble pertenencia

Indexación: Revista UNA

Fernando Pessoa: la insolvencia de ser

Indexación: Revista UNA