Author Archives: admin

134. The First Numerical Modeling of Spontaneous Generation of δ-sunspots

Contributed by Shin Toriumi. Posted on December 17, 2019

In an MHD simulation of flux emergence, a δ-sunspot is formed spontaneously by a collision of areas with opposite polarities. Driven by convective flows and counter-streaming flows, sheared polarity inversion lines form and flux ropes are created above.

133. Hemispherical Asymmetry in the Solar Meridional Flow

Contributed by B. Lekshmi. Posted on October 28, 2019

Subsurface meridional flows from ring-diagram analysis showed a clear hemispheric asymmetry in last 18 years. Interestingly, this flow asymmetry leads the magnetic flux and sunspot number asymmetry by 3.1 – 3.6 years.

129. A Chromospheric Response to the Sunquake generated by the X9.3 Flare of 6 September 2017

Contributed by Sean Quinn. Posted on August 29, 2019

A number of sunquake events were detected in the photosphere after the X9.3 flare of 6 September 2017. This analysis reported the first detection of the chromspheric response to the sunquake events using CaII and Hα observations made by the Swedish 1-meter Solar Telescope.

128. Evolution of Magnetic Helicity in Solar Cycle 24

Contributed by V.V. Pipin. Posted on June 25, 2019

A novel approach is developed to reconstruct the surface magnetic helicity density for the Sun or sun-like stars. The method is applied on the SDO/HMI-observed vector field synoptic data to study the temporal evolution of the Sun’s magnetic helicity density during Solar Cycle 24.

126. Solar Oblateness and Its Variations in Phase with the 22-yr Magnetic Cycle

Contributed by Abdanour Irbah. Posted on June 18, 2019

The Sun’s oblateness shows a variation with solar cycles, in phase with the solar activity level in Cycle 23 but in anti-phase with the activity level in Cycle 24. Such a trend of in-phase during odd cycles and anti-phase during even cycles is confirmed after examining past observations.

125. Solar Farside Magnetograms from Deep Learning Analysis of STEREO/EUVI Data

Contributed by Yong-Jae Moon. Posted on April 29, 2019

A deep learning code is trained using the Sun’s front-side observations, HMI’s magnetograms and AIA’s 304Å EUV images, to establish a relation between magnetic field and EUV flux. Then the code is applied on the STEREO/EUVI 304Å data to obtain the Sun’s far-side magnetic field.