IUCAA Brochure 2024

Gravitational Wave Data Analysis Extracting science out of data from gravitational wave detectors is non-trivial. Smart algorithms are necessary to detect signals in noisy data and to constrain astrophysical models and parameters. Detection of signals from the merger of compact stars relies on the method of matched filtering - a method mastered at IUCAA for application to gravitational wave data analysis. This method relies on prior knowledge of the waveformwhich one is searching for in the data. However, since the waveforms depend on the masses and spins of the component stars, millions of such waveforms need to be searched in the data, which is computationally expensive. The challenge becomes even more when one is looking for interesting sources, like precessesing, eccentric or sub-solar mass binaries, which increases the computation cost bymany folds. A hierarchical search algorithm was proposed for this purpose. IUCAA Scientists recently implemented the algorithm on real data and showed that the search could be performed more than 10 times faster along with a fairly accurate estimation of the noise background, which is necessary to assign significance to the detections. On another front, an algorithm, along with necessary statistical quantifiers, was developed to probe anisotropies of a stochastic gravitational wave background (SGWB) in pixel and spherical harmonic bases. An SGWB can be created due to phenomena in the very early universe and by distant unresolved or unmodelled sources in the nearby universe. SGWB may be detected by LIGO in the next few years, where the above techniques will play a crucial role in characterising the detected signal and for placing precise constraints on the astrophysical parameters.