Related papers: Detecting and Classifying Flares in High-Resolutio…
A convolutional neural network (CNN) is used to construct a new catalog for solar flares based on high resolution (1-s cadence) Geostationary Operational Environmental Satellites (GOES) soft X-ray data. The CNN is trained to identify flare…
In order to efficiently analyse the vast amount of data generated by solar space missions and ground-base instruments, modern machine learning techniques such as decision trees, support vector machines (SVMs) and neural networks can be very…
Exoplanet detection with precise radial velocity (RV) observations is currently limited by spurious RV signals introduced by stellar activity. We show that machine learning techniques such as linear regression and neural networks can…
The H{\alpha} spectral line is a well-studied absorption line revealing properties of the highly structured and dynamic solar chromosphere. Typical features with distinct spectral signatures in H{\alpha} include filaments and prominences,…
The Bent Crystal Spectrometer (BCS) on the NASA Solar Maximum Mission spacecraft observed the X-ray spectra of numerous solar flares during the periods 1980 February to November and 1984~--~1989. The instrument, the first of its kind to use…
Solar flare X-ray emission results from rapidly increasing temperatures and emission measures in flaring active region loops. To date, observations from the X-Ray Sensor (XRS) onboard the Geostationary Operational Environmental Satellite…
Context: Stellar flares have an impact on habitable planets. To relate the observations of the Sun with those of stars, one needs to use a Sun-as-a-star analysis, that is, to degrade the resolution of the Sun to a single point. With the…
Solar flares, the most powerful explosive phenomena in the solar system, may pose significant hazards to spaceborne satellites and ground-based infrastructure. Despite decades of intensive research, reliable flare prediction remains a…
Aims: This paper aims to demonstrate the importance of short-exposure extreme ultraviolet (EUV) observations of solar flares in the study of particle acceleration, heating and energy partition in flares. This work highlights the…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
Studies on the dynamics of solar filaments have significant implications for understanding their formation, evolution, and eruption, which are of great importance for space weather warning and forecasting. The H$\alpha$ Imaging Spectrograph…
The study of nonstationary processes in the Sun is of great interest, and lately, multiwavelength observations and registration of magnetic fields are carried out by means of both ground-based telescopes and several specialized spacecraft…
This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond…
Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual…
Solar storms can have a major impact on the infrastructure of the earth. Some of the causing events are observable from ground in the H{\alpha} spectral line. In this paper we propose a new method for the simultaneous detection of flares…
Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in…
In the third catalog of active galactic nuclei detected by the Fermi-LAT (3LAC) Clean Sample, there are 402 blazars candidates of uncertain type (BCU). Due to the limitations of astronomical observation or intrinsic properties, it is…
A hybrid two-stage machine learning architecture that addresses the problem of excessive false positives (false alarms) in solar flare prediction systems is investigated. The first stage is a convolutional neural network (CNN) model based…
Accurate classification of weather conditions in images is essential for enhancing the performance of object detection and classification models under varying weather conditions. This paper presents a comprehensive study on classifying…
The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the…