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Method of Gravitational Wave Search Based on Adaptive Time-Frequency Analysis and Machine Learning

In simple terms, gravitational waves are ripples in space-time caused by energetic processes in the Universe, such as the movement of mass. One of the exciting things about them is that they can be used to observe systems that are basically impossible to detect using other means. These ripples were predicted by Albert Einstein almost a century ago, but it wasn’t until 2016 that scientists announced, for the first time, the detection of gravitational waves. The Laser Interferometer Gravitational-Wave Observatory (LIGO) is the physics experiment responsible for this detection and it has since continued to make a significant impact in the field. LIGO collaborates closely with the Virgo interferometer; a large interferometer designed to detect gravitational waves, and the Japanese Gravitational Wave Detector in Kamioka Mine (KAGRA), the Large Scale Cryogenic Gravitational Wave Telescope; a project of the gravitational wave studies group led by the Institute for Cosmic Ray Research of The University of Tokyo. But there still remain many unknowns, such as challenges related to the data analysis of gravitational waves. Professor Hirotaka Takahashi is carrying out research on gravitational waves that is attempting to address these challenges by developing algorithms that can dramatically increase the speed and efficiency of gravitational wave searches, which he believes are currently insufficient. Takahashi is a member of the KAGRA collaboration, which, as of March 2020, consists of more than 390 researchers from 90 institutions in 14 countries and regions.