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A general formulation for computing spherical helioseismic sensitivity kernels while incorporating systematical effects
Jishnu Bhattacharya, Shravan M. Hanasoge, Katepalli R. Sreenivasan
As helioseismology matures and turns into a precision science, modeling finite-frequency, geometric and systematical effects is becoming increasingly important. Here we introduce a…
Application and interpretation of deep learning for identifying pre-emergence magnetic-field patterns
Dattaraj B. Dhuri, Shravan M. Hanasoge, Aaron C. Birch +1
Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events, such as flares and…
Validating inversions for toroidal flows using normal-mode coupling
Prasad Subramanian, Shravan Hanasoge
Normal-mode coupling is a helioseismic technique that uses measurements of mode eigenfunctions to infer the interior structure of the Sun. This technique has led to insights into t…
Solar wind prediction using deep learning
Vishal Upendran, M. C. M Cheung, Shravan Hanasoge +1
Emanating from the base of the Sun's corona, the solar wind fills the interplanetary medium with a magnetized stream of charged particles whose interaction with the Earth's magneto…
Supervised convolutional neural networks for classification of flaring and nonflaring active regions using line-of-sight magnetograms
Shamik Bhattacharjee, Rasha Alshehhi, Dattaraj B. Dhuri +1
Solar flares are explosions in the solar atmosphere that release intense bursts of short-wavelength radiation and are capable of producing severe space-weather consequences. Flares…
Sensitivity kernels for inferring Lorentz stresses from normal-mode frequency splittings in the Sun
Srijan Bharati Das, Tuneer Chakraborty, Shravan M. Hanasoge +1
Departures from standard spherically symmetric solar models, in the form of perturbations such as global and local-scale flows and structural asphericities, result in the splitting…