Related papers: Improved detection of farside solar active regions…

We describe a new neural-network technique developed for an automated recognition of solar filaments visible in the hydrogen H-alpha line full disk spectroheliograms. This technique allows neural networks learn from a few image fragments…

The complex dynamics that lead to the emergence of active regions on the sun are poorly understood. One possibility is that magnetic structures (flux tubes, etc.) rise from below the surface by self induction and convection that lead to the…

Time-distance helioseismology and related techniques show great promise for probing the structure and dynamics of the subphotospheric layers of the Sun. Indeed time-distance helioseismology has already been applied to make inferences about…

Intermittent magnetohydrodynamical turbulence is most likely at work in the magnetized solar atmosphere. As a result, an array of scaling and multi-scaling image-processing techniques can be used to measure the expected self-organization of…

We perform helioseismic holography to assess the noise in p-mode travel-time shifts which would form the basis of inferences of large-scale flows throughout the solar convection zone. We also derive the expected travel times from a…

We propose a deep learning algorithm for seismic interface and pocket detection with neural networks trained by synthetic high-frequency displacement data efficiently generated by the frozen Gaussian approximation (FGA). In seismic imaging…

In current seismic acquisition practice, there is an increasing drive for sparsely (in space) acquired data, often in irregular geometry. These surveys can trade off subsurface information for efficiency/cost - creating a problem of…

Magnetohydrodynamic (MHD) seismology uses naturally occurring MHD waves to infer plasma properties that are otherwise hard to measure, especially magnetic field strength and topology, electric currents, fine structuring, transport…

Normal modes are coupled by the presence of perturbations in the Sun, providing a novel and under-appreciated helioseismic technique with which to image the solar interior. The process of measuring coupling between normal modes is…

We present a new deep learning method, dubbed FibrilNet, for tracing chromospheric fibrils in Halpha images of solar observations. Our method consists of a data pre-processing component that prepares training data from a threshold-based…

Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information…

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable…

Helioseismic signatures of dynamo waves have recently been discovered in variations of the solar differential rotation, offering valuable insights into the type of dynamo mechanism operating in the solar convection zone. To characterize…

Transits of habitable planets around solar-like stars are expected to be shallow, and to have long periods, which means low information content. The current bottleneck in the detection of such transits is caused in large part by the…

We developed a solar flare prediction model using a deep neural network (DNN), named Deep Flare Net (DeFN). The model can calculate the probability of flares occurring in the following 24 h in each active region, which is used to determine…

Active regions are open wounds in the Sun's surface. Seismic oscillations from the interior pass through them into the atmosphere, changing their nature in the process to fast and slow magneto-acoustic waves. The fast waves then partially…

Magnetoseismology, a technique of magnetic field diagnostics based on observations of magnetohydrodynamic (MHD) waves, has been widely used to estimate the field strengths of oscillating structures in the solar corona. However, previously…

The Sun supports a rich spectrum of internal waves that are continuously excited by turbulent convection. The GONG network and the MDI/SOHO space instrument provide an exceptional data base of spatially-resolved observations of solar…

Time-distance helioseismology is a set of powerful tools to study features below the Sun's surface. Inverse methods are needed to interpret time-distance measurements, with many examples in the literature. However, techniques that utilize a…

Image super-resolution has been an important subject in image processing and recognition. Here, we present an attention-aided convolutional neural network (CNN) for solar image super-resolution. Our method, named SolarCNN, aims to enhance…