Speaker
Description
A major challenge in LISA data analysis lies in the incomplete characterization of instrumental noise, motivating the development of robust, noise-agnostic detection techniques. In this work, we explore the application of Topological Data Analysis (TDA) as a novel framework for identifying deterministic gravitational wave (GW) signals embedded in noisy data. Our approach leverages Time Delay Embedding to reconstruct time series data as point clouds in higher-dimensional spaces, where deterministic signals manifest as structured geometric shapes, in contrast to the stochastic distributions produced by noise. These structures exhibit topological features—such as loops and voids—whose significance is quantified through persistent homology.
We present results from analyzing LISA Data Challenge data: revealing statistically significant outliers we distinguish deterministic signals from background noise, demonstrating that topological persistence serves as a robust indicator of GW presence even with limited data duration and noise knowledge.
| Parallel session | Gravitational Waves from Binary Systems |
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