Speaker
Description
The Einstein Telescope (ET) will be a key instrument for detecting gravitational waves (GWs) in the coming decades. However, analyzing the data and estimating source parameters will be challenging, especially given the large number of expected detections—between $10^4$ and $10^5$ per year—which makes current methods based on stochastic sampling impractical. In this work, we use DingoIS to perform Neural Posterior Estimation (NPE), a simulation-based inference technique that leverages normalizing flows to approximate the posterior distribution of detected events. After training, inference is fast, requiring only a few minutes per source, and accurate, as validated through importance sampling. We process 1000 randomly selected injections and achieve an average sample efficiency of $\sim 13\%$, which increases to $\sim 18\%$ ($\sim 20\%$) if we consider only sources merging at redshift $z > 4$ ($z > 10$). To confirm previous findings on ET ability to estimate parameters for high-redshift sources, we compare NPE results with predictions from the Fisher information matrix (FIM) approximation. We find that FIM underestimates sky localization errors by a factor of $> 8$, as it does not capture the multimodalities in sky localization introduced by the geometry of the triangular detector. On the contrary, FIM overestimates the uncertainty in luminosity distance by a factor of $\sim 3$ on average when the injected luminosity distance $d^{\mathrm{inj}}_{\mathrm{L}} > 10^5$ Mpc, further confirming that ET will be particularly well suited for studying the early Universe.
| AI keywords | simulation-based inference, normalizing flows, GPUs |
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