Conveners
AI for GW Simulation
- Erik Katsavounidis (MIT)
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54. Representation-Aware Self Supervised Learning for Gravitational Wave Science: Introducing GWCLIPUddipta Bhardwaj (ETH Zurich)5/6/26, 1:20 PMAI for GW SimulationTalk
The next generation of gravitational wave detectors will produce data at a scale and complexity that renders traditional matched-filtering and parameter estimation pipelines insufficient as standalone tools. We argue that the field must shift toward representation-aware learning — building models that acquire rich, physics-informed embeddings of gravitational wave signals rather than...
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Melissa Lopez (Utrecht University)5/6/26, 1:40 PMAI for GW SimulationTalk
Accurate signal models and the disruptive presence of transient noise artifacts (glitches) impose key limitations on the sensitivity of gravitational-wave detectors. Efficient simulation of both is essential for data analysis. Fast generation of gravitational-wave signals is required for detection and parameter estimation, where waveforms are evaluated repeatedly within inference pipelines. At...
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Federico De Santi (University of Milano-Bicocca)5/6/26, 2:00 PMAI for GW SimulationTalk
The LISA space mission, set to launch in the mid 2030s, will open a new window on the “gravitational wave universe”. Thanks to its exceptional sensitivity in the low frequency band ~10⁻⁴–10⁻¹ Hz, it will observe a variety of sources all at the same time: from massive black hole binaries to extreme mass ratio inspirals and Galactic compact binaries. Among these, double white dwarf binaries,...
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Filippo Santoliquido (Gran Sasso Science Institute)5/6/26, 2:20 PMAI for GW SimulationTalk
The coming decade will be crucial for determining the final design and configuration of a global network of next-generation (XG) gravitational-wave (GW) detectors, including the Einstein Telescope (ET) and Cosmic Explorer (CE). In this study and for the first time, we assess the performance of various network configurations using neural posterior estimation (NPE) implemented in Dingo-IS-a...
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Anastasios Theodoropoulos5/6/26, 2:40 PMAI for GW SimulationTalk
The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate models for BBH waveforms have been proposed. Those offer the potential to dramatically accelerate waveform generation while maintaining accuracy competitive with that of traditional waveform approximants. In this work, we...
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