Speaker
Description
Gravitational Wave (GW) Physics has entered a new era of Multi-Messenger Astronomy (MMA), characterized by increasing GW event detections from GW observatories at the LIGO-Virgo-KAGRA collaborations. This presentation will introduce the KAGRA experiment, outlining the current workflow from data collection to physics interpretation, and demonstrate the transformative role of machine learning (ML) in some GW data analysis.
This talk also bridge advancements in computational techniques between fundamental research in Astrophysics and High-Energy Physics (HEP). Such initiatives may find some common interests in the context of next generation trigger systems in HEP and advanced signal processing. Innovative solutions for addressing next-generation data analysis challenges will be presented, with a focus on the use of modern ML tools within the ROOT C++ Framework (CERN) and introducing Anaconda HEP-Forge for rapid software deployments. These tools, available as an additional shared libraries in ROOT, integrate key requirements for typical astrophysical analysis, but also HEP physics analysis — such as complex filtering, vector manipulation, KAFKA & other Cloud data transfers, and complex tensor computations on both CPU and GPU technologies.
