16–20 Jun 2025
THotel, Cagliari, Sardinia, Italy
Europe/Rome timezone

✨ Rapid Identification and Classification of Eccentric Binary Blackhole mergers using Machine Learning

17 Jun 2025, 17:32
3m
T3a

T3a

Poster Session B Real-Time Data Processing 🔀 Real-time Data Processing

Speaker

Yuvraj Sharma

Description

The future of Gravitational Wave (GW) detectors [LVK] have made remarkable progress, with an expanding sensitivity band and the promise of exponential increase in detection rates for upcoming observing runs [O4 and beyond]. Among the diverse sources of GW signals, eccentric Binary mergers present an intriguing and computationally challenging aspect. We address the imperative need for efficient detection and classification of eccentric Binary mergers using Machine Learning (ML) techniques. Traditional Bayesian Parameter estimation methods, while accurate, can be prohibitively time-consuming and computationally expensive. To overcome this challenge, we leverage the capabilities of ML to expedite the identification and classification of eccentric GW events. I will present our approach that employs Separable Convolutional Neural Networks (SCNN) to discriminate between non-eccentric and eccentric Binary mergers and further classifying the latter into categories of low, moderate, and high eccentricity mergers.

AI keywords CNN, Separable CNN, Neural Networks

Primary author

Co-author

Dr Prayush Kumar (International Centre for Theoretical Sciences, Bangalore)

Presentation materials

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