GW-SkyLocator is a deep learning model designed for the rapid sky localization of gravitational wave (GW) sources from compact binary coalescences, such as binary black holes (BBH), binary neutron star (BNS) and neutron star-black hole (NSBH) mergers. These events often produce electromagnetic (EM) counterparts, making prompt localization critical for multimessenger astronomy. Traditional localization methods, which rely on Bayesian approaches, can be slow, taking anywhere from seconds to days, which poses challenges for detecting short-lived EM signals like gamma-ray bursts.
GW-SkyLocator uses a neural network architecture based on normalizing flows and ResNet, trained on signal-to-noise (S/N) time series derived from matched-filtering techniques. By using the S/N time series instead of the full GW strain data, the model efficiently extracts features, allowing it to infer sky location posteriors within seconds. This approach not only enhances the speed of localization but also supports premerger localization for BNS events, providing early warnings up to a minute before the merger. The premerger capabilities open up opportunities for EM follow-up of precursor emissions, enabling astronomers to observe a broader spectrum of phenomena associated with compact binary mergers.
Overall, the work showcases a significant advancement in the application of deep learning for GW astronomy, achieving rapid localization that supports both prompt and premerger observations, crucial for multimessenger studies.
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Dr. Chayan Chatterjee
AI for New Messengers Postdoctoral Fellow
Dr. Chayan Chatterjee is the A.I. for New Messengers Postdoctoral Fellow at Vanderbilt University. His research focuses on application of machine learning to study gravitational waves - tiny ripples in space-time created by colliding black holes and neutron stars.
