Lifetimes and rotation within the solar mean magnetic field

ABSTRACT We have used very high-cadence (sub-minute) observations of the solar mean magnetic field (SMMF) from the Birmingham Solar Oscillations Network (BiSON) to investigate the morphology of the SMMF. The observations span a period from 1992 to 2012, and the high-cadence observations allowed the exploration of the power spectrum up to frequencies in the mHz range. The power spectrum contains several broad peaks from a rotationally modulated (RM) component, whose linewidths allowed us to measure, for the first time, the lifetime of the RM source. There is an additional broadband, background component in the power spectrum which we have shown is an artefact of power aliasing due to the low fill of the data. The sidereal rotation period of the RM component was measured as 25.23 ± 0.11 d and suggests that the signal is sensitive to a time-averaged latitude of ∼12°. We have also shown the RM lifetime to be 139.6 ± 18.5 d. This provides evidence to suggest that the RM component of the SMMF is connected to magnetic flux concentrations (MFCs) and active regions (ARs) of magnetic flux, based both on its lifetime and location on the solar disc.

Background Estimation Using Principal Component Analysis Based on Limited Memory Block Krylov Subspace Optimization

<p>Given a video of 𝑀 frames of size ℎ × 𝑤. Background components of a video are the elements matrix which relative constant over 𝑀 frames. In PCA (principal component analysis) method these elements are referred as “principal components”. In video processing, background subtraction means excision of background component from the video. PCA method is used to get the background component. This method transforms 3 dimensions video (ℎ × 𝑤 × 𝑀) into 2 dimensions one (𝑁 × 𝑀), where 𝑁 is a linear array of size ℎ × 𝑤. The principal components are the dominant eigenvectors which are the basis of an eigenspace. The limited memory block Krylov subspace optimization then is proposed to improve performance the computation. Background estimation is obtained as the projection each input image (the first frame at each sequence image) onto space expanded principal component. The procedure was run for the standard dataset namely SBI (Scene Background Initialization) dataset consisting of 8 videos with interval resolution [146 150, 352 240], total frame [258,500]. The performances are shown with 8 metrics, especially (in average for 8 videos) percentage of error pixels (0.24%), the percentage of clustered error pixels (0.21%), multiscale structural similarity index (0.88 form maximum 1), and running time (61.68 seconds). </p>