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Mr Siddharth Soni (University of California, Riverside)5/6/26, 4:20 PMAI for Real-Time Data ProcessingTalk
The detection of gravitational waves by the Laser Interferometer Gravitational-Wave Observatory (LIGO) has opened a new window onto the universe, but the sensitivity of these detectors is fundamentally limited by a complex and evolving landscape of instrumental and environmental noise. In recent years, machine learning has emerged as a powerful tool for understanding, characterizing, and...
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Deep Chatterjee5/6/26, 4:40 PMAI for Real-Time Data ProcessingTalk
The number of GW events have increased from two real-time detections in the LIGO first observing run, to over two hundred in the LIGO-Virgo-KAGRA fourth observing run. In parallel, the last decade has also seen the increased use of machine learning, especially neural networks, in science. For the first time, after a decade of discovery, binary black holes (BBHs) are routinely detected by...
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Martin Gerini (UCLouvain)5/8/26, 9:00 AMAI for Real-Time Data ProcessingTalk
Gravitational waves, ripples in the fabric of spacetime produced by accelerating cosmic masses, are routinely detected by the ground-based LIGO-Virgo-KAGRA (LVK) network. The historic first observation of a Binary Neutron Star (BNS) coalescence, GW170817, was tracked in the detectors’ sensitive band for about tens of seconds. This observable duration is dictated by the interferometers’ minimum...
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Nikolas Moustakidis (Aristotle University of Thessaloniki), Mr Theofilos Moustakidis (University of Thessaly)5/8/26, 9:20 AMAI for Real-Time Data ProcessingTalk
Gravitational-wave transient searches in LIGO-Virgo-KAGRA (LVK) routinely run multiple low-latency (and offline) pipelines in parallel. Their redundancy and complementarity improves robustness, but it also creates a practical challenge: how to combine pipeline outputs into a single, reliable detection statistic without resorting to mere union of their findings. We present BOAW...
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Daniel Lanchares (Universidad de Oviedo - ICTEA)5/8/26, 9:40 AMAI for Real-Time Data ProcessingTalk
The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this work we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work, we map BBH spectrograms from the Advanced LIGO and Advanced Virgo...
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Ana Caramete (Institute of Space Science - INFLPR Subsidiary)5/8/26, 10:00 AMAI for Real-Time Data ProcessingTalk
With the LISA mission formally adopted by ESA in January 2024 and now in its implementation phase, preparations across the science ground segment are accelerating toward launch in the mid‑2030s. A central priority is ensuring that data‑analysis tools are ready to extract science quickly and reliably once telemetry becomes available.
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Within this context, here we present GWEEP(Gravitational... -
Michael Coughlin (University of Minnesota)5/8/26, 10:20 AMAI for Real-Time Data ProcessingTalk
With the detection of compact binary coalescences and their
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electromagnetic counterparts by gravitational-wave detectors, a new
era of multi-messenger astronomy has begun. In this talk, I will
describe how machine learning is enabling the gravitational-wave community to make very low-latency detection and parameter estimation possible within the alert system. I will then discuss how current...
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