Abstract Details

Name: Muthu Priyal
Affiliation: Indian Institute of Astrophysics, Bangalore
Conference ID: ASI2021_246
Title : Periodic and Quasi-Periodic Variations in the Ca K Index During the 20th Century Using Kodaikanal Data
Authors and Co-Authors : Muthu Priyal, Jagdev Singh , B. Ravindra , Indian Institute of Astrophysics, Bengaluru, INDIA
Abstract Type : Oral
Abstract Category : Sun and the Solar System
Abstract : We have digitized the Ca K images obtained at Kodaikanal Observatory with a pixel resolution of 0.86 arcsec and 16-bit readout to achieve better spatial and photometric accuracy. In addition to the general photometric analysis procedure carried out on the data, we have corrected these digitized images for instrumental effects. Then, we have normalized all the images considering their intensity distribution. Afterwards, we separate the images into two groups considering the width of the intensity distribution and their visual quality. Group I contains uniform time series of images taken at the center of the Ca K line and the other group contains the remaining images. We study the variation in the Ca K index with time for both data sets. Comparing the results we find that it is necessary to select the images to generate uniform time series to investigate periodic variations. We find that uniform time series termed as “Good” shows well-defined 11-year periodicity in the Ca K average intensity. In addition, the fast Fourier transform and wavelet analysis of the data show a quasi-periodicity of ≈ 3 years that may be due to the duration of the active phase of the solar cycle. The time series with non-uniform images termed as “Okay” shows a large scatter in the average intensity and affects the amplitude of the activity. This series also shows a number of mid-term quasi-periodicities of short duration in the wavelet analysis, probably due to the non-uniform quality of the images. This methodology will be also useful to combine the data from different observatories and generate a uniform time series with less gaps in the data.