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Learn about GARUDA powered with TraP, a deep learning-based data reduction pipeline for fast radio transient detection, presented in this 21-minute conference talk from the FTSky program at the International Centre for Theoretical Sciences. Discover how this innovative pipeline combines the GARUDA computing infrastructure with the Transients Pipeline (TraP) framework to process and analyze large volumes of radio astronomy data using artificial intelligence techniques. Explore the technical implementation of deep learning algorithms for identifying and characterizing fast radio transients, including Fast Radio Bursts (FRBs), Rotating Radio Transients (RRATs), and other cosmic phenomena. Understand the computational challenges involved in real-time processing of radio telescope data and how machine learning approaches can improve detection sensitivity and reduce false positive rates. Gain insights into the integration of AI-driven methodologies within existing astronomical data processing workflows and their potential impact on advancing our understanding of mysterious cosmic transient events. This presentation is part of the Fast Radio Transient Sky program, which focuses on developing next-generation techniques for studying fast radio transients across multiple wavelengths and fostering collaboration between academia and industry in astrophysics research.
