Name: Sagar Kumar Gupta
Affiliation: Indian Institute of Technology Bombay
Conference ID : ASI2022_618
Title : Application of Gaussian mixture modeling in Intermediate Mass Black Hole search
Authors : Sagar Kumar Gupta Archana Pai Ik Siong Heng Christopher Messenger V. Gayathri Dixeena Lopez
Abstract Type: Poster
Abstract Category : General Relativity and Cosmology
Abstract : The data obtained from the Gravitational Wave (GW) detectors is non-stationary and is plagued with non-Gaussian noise, which often contains spurious noise transients (glitches). The change in nature of these glitches as the sensitivity of the detectors changes makes the task of detecting transient GW signals in a noise plagued data even more challenging. For a given dataset, transient gravitational wave searches produce a corresponding list of triggers corresponding to a gravitational wave signal. These triggers could often result from glitches mimicking gravitational wave characteristics if the transient is short in nature, affecting the sensitivity of the search for systems such as short-duration bursts or signals from very massive compact binaries. To distinguish glitches from GW signals, search algorithms like coherent WaveBurst apply thresholds on the trigger properties. Here, we present the Gaussian Mixture Models, a supervised machine learning approach, as a means of modeling the multi-dimensional trigger attribute space. We fine-tune and optimize the model for Intermediate Mass Black Hole (IMBH) detection and show an improvement in sensitivity, especially towards high mass and high mass ratio binary systems.