Abstract

Astrophysical Insights through Gravitational Wave Data Analysis: Data Preprocessing

Author(s): Shufan Dong*

This paper presents a distinct approach to the analysis of Gravitational Wave (GW) data, integrating astrophysical theories and computational programming. The study focuses on the preprocessing, noise filtering, and visualization of GW signals, introducing advanced data analysis methods to extract meaningful information and features from the raw GW data. The methodology involves downloading GW data from the GW Open Science Center and processing GW data, handling missing values, applying noise filtration, normalizing data and employing various plotting techniques to inspect the GW data. Although Machine Learning (ML) is not applied in this paper, the data preprocessing methods discussed are crucial for future ML applications in GW astronomy.