| Name: | Pranjali Sharma |
| Affiliation: | Indian Institute of Technology , Indore |
| Conference ID : | ASI2023_516 |
| Title : | Study of light curves using machine learning techniques |
| Authors : | Pranjali Sharma, Sushmita Agarwal, Amit Shukla, Saurabh Das |
| Mode of Presentation: | Poster |
| Abstract Category : | Sun and the Solar System |
| Abstract : | Understanding periodicity and time series trends is essential to studying various astronomical sources. The light curve parameters' extraction often depends on how well the data is sampled. In the case of unevenly sampled data, signals may arise due to uninformed techniques, which could then be mistaken for having an astrophysical origin. We make use of a non-parametric machine learning technique called Gaussian Process Regression (GPR) to model the light curves of various sources, such as sunspots, AGNs, and variable stars, to extract meaningful physical parameters in the current work. We also forecast the upcoming 25th solar maximum using GPR. |
