Abstract Details

Name: Vivek Singh
Affiliation: Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, UP PIN Code 211007
Conference ID: ASI2021_194
Title : A comparison between autocorrelation and Lomb-Scargle periodogram analysis techniques in estimating solar rotation
Authors and Co-Authors : Vivek Kumar Singh SHUATS, Prayagraj, UP, India Sanish Thomas SHUATS, Prayagraj, UP, India Satish Chandra, Pt. Prithi Nath P G College, Kanpur, India
Abstract Type : Poster
Abstract Category : Sun and the Solar System
Abstract : Solar rotation, accountable for the solar activity cycle is one of the most important characteristics. Due to advancement of techniques and availability of data, the studies on rotation of Sun and other stars are increasing. Sun is only star whose rotation is investigated in detail and result is served as reference for estimation of rotation of other sun like stars. Sun exhibits differential rotation as a function of latitude as well as altitude both. To determine solar rotation rate different methods as, tracking of tracers, spectral analysis, heliosiesmology and flux modulation are used. In flux modulation method, a time series is generated for the period of study. The harmonics present in the time series are estimated by various statistical methods as autocorrelation (Vats et. al. 1998, 2001, Bhatt et. al 2017, 2018), wavelet analysis (Hempelmann A., 2002, Freitas, D.B. et. al. 2010), Fast Fourier Transform (Hempelmann A., 2002, Kuker M. 2008) and Lomb Scargle periodogram (Giordano S.,2008,Mancuso S., ‎2020,Li et.al 2020). Present work is a comparison between autocorrelation and LSP to estimate periodicities present in a time series. A typical time series is generated for the radio flux data observed at Segamore Observatory for year (1972) and X-ray (SFD) data observed by HINODE for year (2013). Harmonics present in the time series are evaluated by autocorrelation and LSP method both. Five different time series are generated by artificial data chosen randomly with 5%, 10%, 15%, 20% and 25% data gaps. The time series generated by these datasets are used to find periodicities by autocorrelation and LSP method. The estimated rotation periods are compared with a result obtained by a time series generated with continuous data. The result suggests LSP is able to deal the time series with data gaps also. The detail comparison results would be presented in the paper.