Filling Factors of Sunspots in SODISM Images
Received: 1st December 2018; Accepted: 18th February 2019; Published: 1st April 2019 Abstract: The calculated filling factors (FFs) for a feature reflect the fraction of the solar disc covered by that feature, and the assignment of reference synthetic spectra. In this paper, the FFs, specified as a function of radial position on the solar disc, are computed for each image in a tabular form. The filling factor (FF) is an important parameter and is defined as the fraction of area in a pixel covered with the magnetic field, whereas the rest of the area in the pixel is field-free. However, this does not provide extensive information about the experiments conducted on tens or hundreds of such images. This is the first time that filling factors for SODISM images have been catalogued in tabular formation. This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Data and models for sunspots detection in solar images captured with smart telescopes
Observing the sun with Electronically Assisted Astronomy allows understanding of solar phenomena, contributes to scientific research, promotes science education, and provides recreational enjoyment, all while requiring s…
AstronomyDeep LearningImage patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis
The flare-productivity of an active region is observed to be related to its spatial complexity. Mount Wilson or McIntosh sunspot classifications measure such complexity but in a categorical way, and may therefore not use…
ClusteringDictionary LearningFast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
The volume of data being collected in solar physics has exponentially increased over the past decade and with the introduction of the $\textit{Daniel K. Inouye Solar Telescope}$ (DKIST) we will be entering the age of pet…
General Classificationimage-classificationImage ClassificationTransfer LearningImage patch analysis and clustering of sunspots: a dimensionality reduction approach
Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images. Their complexity is correlated with explosive solar activity and so c…
ClusteringDimensionality ReductionA Machine Learning and Computer Vision Approach to Geomagnetic Storm Forecasting
Geomagnetic storms, disturbances of Earth's magnetosphere caused by masses of charged particles being emitted from the Sun, are an uncontrollable threat to modern technology. Notably, they have the potential to damage sa…
BIG-bench Machine Learning