Nihar Gupte

In the last 10 years Gravitational Waves (GWs) have changed our understanding of Binary Black Holes (BBHs) and Binary Neutron Stars (BNSs). However, as the number of GW observations grow and the complexity of waveform models increases, we need fast and accurate inference methods if we are to analyze the catalog of BBH mergers. In this seminar, I will present results from DINGO, a machine-learning pipeline which is able to quickly and accurate infer the properties of BBHs and BNSs. First, I will present the inference of eccentric BBHs, showing that there are several candidates in O3 and O4a which have signs of eccentricity. I will then show how one can combine these observations to make population level statements about eccentricity. Finally, I will showcase the recent advances/implementations in DINGO led by my collaborators which allow for inference on BNS systems, lensed GWs and next generation ground based detectors.