Comparing next-generation detector configurations for high-redshift gravitational wave sources with neural posterior estimation

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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 method based on normalizing flows and importance sampling that enables fast and accurate inference. We focus on a specific science case involving short-duration, massive and high-redshift binary black hole (BBH) mergers with detector-frame chirp masses ($\mathcal{M}_\mathrm{d}$) > 100 M$_\odot$. These systems encompass early-Universe stellar and primordial black holes, as well as intermediate-mass black-hole binaries, for which XG observatories are expected to deliver major discoveries. Validation against standard Bayesian inference demonstrates that NPE robustly reproduces complex and disconnected posterior structures across all network configurations. For a network of two misaligned L-shaped ET detectors (2L MisA), the posterior distributions on luminosity distance can become multimodal and degenerate with the sky position, leading to less precise distance estimates compared to the triangular ET configuration. However, the number of sky-location multimodalities is substantially lower than the eight expected with the triangular ET, resulting in improved sky and volume localization. Adding CE to the network further reduces sky-position degeneracies, and the better performance of the 2L MisA configuration over the triangle remains evident.

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