Solar radio bursts are some of the brightest emissions at radio frequencies in the solar system. The emission mechanisms that generate these bursts offer a remote insight into physical processes in solar coronal plasma, while fine spectral features hint at its underlying turbulent nature. During radio noise storms many hundreds of solar radio bursts can occur over the course of a few hours. Identifying and classifying solar radio bursts is often done manually although a number of automatic algorithms have been produced for this purpose. The use of machine learning algorithms for image segmentation and classification is well established and has shown promising results in the case of identifying Type II and Type III solar radio bursts. Here we present the results of a convolutional neural network applied to dynamic spectra of NenuFAR solar observations. We highlight some initial success in segmenting radio bursts from the background spectra and outline the steps necessary for burst classification.
Eruptive activity in the solar corona can often lead to the propagation of shock waves. In the radio domain the primary signature of such shocks are type II radio bursts, observed in dynamic spectra as bands of emission slowly drifting towards lower frequencies over time. These radio bursts can sometimes have inhomogeneous and fragmented fine structure, but the cause of this fine structure is currently unclear. Here we observe a type II radio burst on 2019-March-20th using the New Extension in Nançay Upgrading LOFAR (NenuFAR), a radio interferometer observing between 10-85 MHz. We show that th…
We design an event recognition-analysis system that can automatically detect solar type III radio burst and can mine information of the burst from the dynamic spectra observed by Nancay Decameter Array (NDA). We investigate the frequency drift rate of type III bursts and the speed of electron beams responsible for the generation of the bursts. Several computer vision methods are used in this automatic analysis system. The Hough transform is performed to recognize the line segment associated with type III bursts in the dynamic spectra. A modified active contour model is used to track the backbo…
Young stellar objects in their pre-main sequence phase are characterized by irregular changes in brightness, generally attributed to an increase of the mass accretion rate due to various kind of instabilities occurring in the circumstellar disk. In the era of large surveys aimed to monitor the sky, we present a pipeline to detect irregular bursts, in particular EXors-like ( EX Lupi type eruptive variables), in the light curves. The procedure follows a heuristic approach and is tested against the light curves already collected for a few objects presently recognized as bona fide or candidate EXo…
3D MHD simulations of coronal loops heated via magnetic braiding II. Automatic detection of reconnection outflows and statistical analysis of their properties
Aims. We examined the characteristics of isolated intermediate drift bursts (IMDs) and their morphologies on dynamic spectra, in particular the positioning of emission and absorption ridges and the repetition rate of fiber groups. These were compared with a model in order to determine the conditions under which the IMDs appear and exhibit the above characteristics. Methods. We analyzed sixteen metric type IV events with embedded IMDs. The events were recorded with the Artemis-JLS/SAO high resolution (10 ms cadence) receiver in the 270-450MHz range with a frequency resolution of 1.4 MHz. We dev…
Radio bursts from the solar corona can provide clues to forecast space weather hazards. After recent technology advancements, regular monitoring of radio bursts has increased and large observational data sets are produced. Hence, manual identification and classification of them is a challenging task. In this paper, we describe an algorithm to automatically identify radio bursts from dynamic solar radio spectrograms using a novel statistical method. We used e-CALLISTO radio spectrometer data observed at Gauribidanur observatory near Bangalore in India during 2013 - 2014. We have studied the cla…
Implementation of a Near-Realtime Recording and Reporting System of Solar Radio Bursts
New Jersey Institute of Technology（新泽理工学院）；Owens Valley Radio Observatory, California Institute of Technology（加州理工学院欧文谷天文台）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Arizona State University（亚利桑那州立大学）；Cahill Center for Astronomy and Astrophysics, California Institute of Technology（加州理工学院凯尔天文学与天体物理学中心）
Athlone Institute of Technology（阿斯隆理工学院）；Dublin Institute of Advanced Studies（都柏林高等研究院）
Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterisation, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting p…
CALLISTO is a radio spectrometer designed to monitor the transient radio emissions/bursts originated from the solar corona in the frequency range $45-870$ MHz. At present, there are $\gtrsim 150$ stations (together forms an e-CALLISTO network) around the globe continuously monitoring the Sun 24 hours a day. We have developed a pyCallisto, a python library to process the CALLISTO data observed by all stations of the e-CALLISTO network. In this article, we demonstrate various useful functions that are routinely used to process the CALLISTO data with suitable examples. This library is not only ef…
Automatic detection of Flare Ribbon Fine Structures as Proxies for Plasmoid Dynamics in Flare Reconnection
Department of Mathematical Sciences, Durham University（达勒姆大学数学科学系）；Heliophysics Science Division, NASA Goddard Space Flight Center（NASA戈达德太空飞行中心日球物理学部）；Astronomy Department, University of Maryland, College Park（马里兰大学天文学系）；Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Glasgow（格拉斯哥大学物理与天文学学院苏格兰大学物理联盟）；Rosseland Centre for Solar Physics, University of Oslo（奥斯陆大学罗瑟兰太阳物理学中心）
The proper classification of plasma regions in near-Earth space is crucial to perform unambiguous statistical studies of fundamental plasma processes such as shocks, magnetic reconnection, waves and turbulence, jets and their combinations. The majority of available studies have been performed by using human-driven methods, such as visual data selection or the application of predefined thresholds to different observable plasma quantities. While human-driven methods have allowed performing many statistical studies, these methods are often time-consuming and can introduce important biases. On the…
