Real-time Gravitational Wave data analysis with Machine Learning

by Katya Govorkova (Massachusetts Inst. of Technology (US))

Wednesday 12 Feb 2025, 11:00 → 12:00 Europe/Zurich

222/R-001 (CERN)

In recent years, deep learning algorithms have excelled in various domains, including Astronomy. Despite this success, few deep learning models are planned for online deployment in the O4 data collection run of the LIGO-Virgo-KAGRA collaboration. This is partly due to a lack of standardized software tools for quick implementation and deployment of novel ideas with confidence in production performance. Our team addressed this gap by developing ml4gw and hermes libraries. We’ll discuss how these libraries enhanced efficiency and model robustness in several applications: Aframe, a low-latency machine learning pipeline for compact binary sources of gravitational waves, and a deep learning-based denoising scheme for astrophysical gravitational waves, covering Binary Neutron Stars (BNS), Neutron Star-Black Hole (NSBH), and Binary Black Hole (BBH) events. We'll explore the potential of machine learning for real-time detection and end-to-end searches for gravitational-wave transients. We also introduce anomaly detection techniques using deep recurrent autoencoders and a semi-supervised strategy called Gravitational Wave Anomalous Knowledge (GWAK) to identify binaries, detector glitches, and hypothesized astrophysical sources emitting GWs in the LIGO-Virgo-KAGRA frequency band. We discuss how in the future these developments can lead to rapid deployment of next-generation deep learning technology for fast gravitational wave detection.

Bio: Katya Govorkova completed her PhD at Nikhef with LHCb experiment, where she conducted high-precision physics measurements and contributed to the development of a fully software-based trigger system. Later, as a CERN Fellow, she worked on the CMS experiment, developing and deploying machine learning techniques for anomaly detection in the hardware trigger. Currently, she is a Postdoctoral Associate at MIT, where she develops anomaly detection techniques for the LIGO trigger system, enabling advancements in Multi-messenger Astronomy. Katya recently returned to LHCb, focusing on FPGA-based solutions for real-time data processing as part of the LHCb Upgrade II.

Coffee will be served at 10:30.