Speaker
Description
Deep Learning (DL) applications for gravitational wave (GW) physics are becoming increasingly common without the infrastructure to validate them at scale or deploy them in real-time. The challenge of gravitational waves requires and real-time time series workflow. With ever more sensitive GW observing runs beginning in 2023-5 and progressing through the next decade, ever-increasing sensitivity will be present, demanding real-time robust processing of GW experimental pipelines. We present ml4gw, an end-to-end software framework for optimized training and ML inference for real-time gravitational wave processing. This framework is rapidly being adopted for many applications, including denoising, binary black detection, anomaly detection, and real-time parameter estimation. These tools allow for the development of deep learning-powered GW physics applications, which are faster, more intuitive, and better able to leverage the powerful modeling techniques available in the GW literature. We present the ML4GW toolkit and discuss how it optimally leverages heterogeneous computing. Finally, we discuss the future of real-time heterogeneously computing within GW detection and how it can be used to probe our ever-expanding universe.
| Focus areas | MMA |
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