Speaker
Description
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 frequency limit. While current-generation facilities are restricted by seismic noise to frequencies above ≈ 20 Hz, the advent of Third-Generation (3G) observatories, such as the Einstein Telescope (ET) or the Cosmic Explorer (CE), will push this boundary down to the few-Hertz regime, enabling the observation of BNS coalescences for several hours before merger. Over these extended observation windows, the standard assumption of a static detector breaks down. The Earth’s rotation and orbital motion induce amplitude and frequency modulations such as Doppler shifts and antenna pattern variations. Tracking these over long durations requires unaffordable computing costs, limiting the scalability and low-latency applicability of classical algorithms, such as Matched Filtering.
To reduce this computational burden, we propose marginalizing over the extrinsic parameters using machine learning, restricting the pipeline to a mass-only search. To achieve this, we design a 2D Convolutional Neural Network (CNN) that processes multi-detector, complex Short Fourier Transform (SFT) tensors. By optimizing the Binary Cross-Entropy (BCE) loss, the network approximates the optimal Bayesian detection statistic, allowing the CNN to learn the non-linear time-frequency morphology of the signal and implicitly performing the required marginalization during training. We conduct a large-scale campaign of software-simulated signals to compare the CNN’s performance with a GPU-optimized SFT-based Matched Filtering baseline across three signal duration regimes: 𝑇sig ∼ 25 minutes (𝑓min = 5 Hz), ∼ 1.88 hours (3 Hz), and ∼ 6 hours (2 Hz).
We found that the CNN achieves a detection sensitivity comparable to the Matched Filtering baseline, while reducing the overall computational cost by over four orders of magnitude and slashing inference latency from minutes to milliseconds. This gain in efficiency enables a low-latency triggering of long-duration gravitational wave signals, suggesting Machine Learning as a crucial tool for earlywarning multi-messenger astronomy in the upcoming 3G era.