Deledda · Deep Learning the Dark Universe with Gravitational Waves

Deep Learning the Dark Universe with Gravitational Waves

Over the past decade, gravitational-wave (GW) astronomy has opened an entirely new window on the Universe. By observing the tiny ripples in spacetime produced by the mergers of compact objects such as black holes and neutron stars, scientists can probe gravity in its most extreme regimes and explore the population and evolution of massive objects across cosmic time. In parallel, a global effort has emerged to detect nanohertz gravitational waves through pulsar timing arrays (PTAs), which are sensitive to signals from supermassive black hole binaries in the centers of galaxies. In 2023, the four major PTA collaborations jointly announced compelling evidence for a stochastic gravitational-wave background. Two year later, the LIGO–Virgo–KAGRA (LVK) collaboration released the first part of its fourth observing run catalogue (O4a), containing more than a hundred new compact binary coalescences. These results mark a transformative moment for the field, but they also highlight a major challenge: the exponential growth of data is making traditional analysis methods computationally unsustainable. The Deledda project—Deep Learning the Dark Universe with Gravitational Waves—addresses this challenge by developing advanced machine learning tools to accelerate and improve the analysis of gravitational-wave data. The project builds on the rapid progress of deep learning and simulation-based inference to make parameter estimation faster, more reliable, and more interpretable. In current pipelines, obtaining the physical parameters of a GW source can take from days to weeks of computation on large clusters, limiting the number of events that can be fully characterized and delaying possible multimessenger follow-ups. Deledda aims to replace these costly processes with neural methods that learn from simulated data and can perform inference in seconds, thus unlocking the full scientific potential of current and future GW detectors. Within this framework, the project pursues three complementary objectives. The first is to integrate physical symmetries and domain knowledge into neural architectures for compact-binary mergers, leading to the development of a simulation-based inference model (Labrador) that achieves high accuracy and interpretability while training within only one day on modern GPUs. The second is to explore alternative inference strategies for PTA datasets, introducing a fast variational inference approach that can analyze the 15-year NANOGrav dataset in minutes instead of days, enabling new studies of the low-frequency gravitational-wave background. The third is to improve the estimation of Bayesian evidence—a key quantity for model selection—through a novel normalizing-flow method (floZ), which is robust and scalable to high-dimensional problems. By combining expertise in gravitational-wave physics and modern machine learning, Deledda contributes to a new generation of analysis methods that can keep pace with the rapidly expanding GW Universe. The project’s outcomes are expected to enhance the scientific return of large international observatories such as LVK and PTA collaborations, reduce computational costs, and promote the broader integration of AI techniques in fundamental physics research.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз