Next-generation Detectors Enable Observation of Early Universe Black Hole Mergers at High Redshift

Detecting gravitational waves from the earliest moments of the universe requires optimising the design of future observatories, and a new study investigates how different configurations of next-generation detectors will perform. Filippo Santoliquido, Jacopo Tissino, and Ulyana Dupletsa, from the Gran Sasso Science Institute, alongside colleagues including Marica Branchesi and Jan Harms, present a detailed comparison of potential detector networks, focusing on their ability to pinpoint the sources of high-redshift binary black hole mergers. The team employs a novel technique called neural posterior estimation, which rapidly and accurately determines the characteristics of these events, and demonstrates its reliability against traditional analytical methods. Their results reveal that a network of two L-shaped detectors offers surprisingly effective sky localisation, even outperforming a triangular configuration, and that incorporating the Cosmic Explorer further enhances precision, promising significant advances in our understanding of black holes and the early cosmos.

This method, implemented through the Dingo-IS framework, enables fast and accurate inference of gravitational-wave source properties, crucial for evaluating network performance.

Experiments employed simulated signals from binary black hole mergers, processed through various detector network layouts, including two misaligned L-shaped Einstein Telescope detectors and a triangular Einstein Telescope configuration. Researchers then compared the precision with which they could determine the location and distance to these simulated events, further augmenting the network with the Cosmic Explorer detector to quantify its impact on reducing uncertainties in sky position and distance estimation.

The results reveal that a network of two L-shaped detectors offers surprisingly effective sky localisation, even outperforming a triangular configuration, and that incorporating the Cosmic Explorer further enhances precision. This innovative methodological approach provides critical insights for optimising the design of future gravitational-wave observatories and maximising their scientific potential, promising significant advances in our understanding of black holes and the early cosmos.

Neural Estimation of Binary Black Hole Parameters

Scientists have achieved robust performance in estimating parameters from gravitational wave signals using a novel neural posterior estimation method, implemented within the Dingo-IS framework. This work assesses various configurations of next-generation gravitational wave detectors, including the Einstein Telescope and Cosmic Explorer, focusing on short-duration, massive, and high-redshift binary black hole mergers.

The study demonstrates that the method accurately reproduces complex posterior distributions across all network configurations when validated against standard Bayesian inference techniques, with sample efficiencies exceeding 1% for a substantial portion of injected signals. Analysis of simulated binary black hole mergers shows that the method efficiently explores parameter space even for challenging signals, achieving optimal signal-to-noise ratios exceeding 40, and up to 70 in certain configurations.

Measurements confirm that the two-detector misaligned L-shaped Einstein Telescope configuration exhibits a lower number of sky-location multimodalities compared to the triangular Einstein Telescope configuration, resulting in improved sky and volume localisation. Adding Cosmic Explorer to the network further refines sky-position estimates, and the superior performance of the misaligned configuration remains evident, delivering enhanced source localisation capabilities.

This breakthrough delivers a powerful tool for analysing data from future gravitational wave observatories and unlocking discoveries about early-Universe stellar and primordial black holes, as well as intermediate-mass black-hole binaries.

Einstein Telescope Network Performance Evaluated

This research presents a novel assessment of potential configurations for future gravitational-wave observatories, specifically focusing on networks incorporating the Einstein Telescope and Cosmic Explorer. Scientists employed neural posterior estimation to evaluate how different detector arrangements perform when searching for signals from distant, massive black hole mergers, a challenging task crucial for understanding early universe phenomena and intermediate-mass black holes.

The team rigorously validated this new method against traditional Bayesian inference, confirming its ability to accurately reconstruct complex probability distributions for signal parameters. The results demonstrate that a network of two misaligned, L-shaped Einstein Telescopes outperforms a triangular Einstein Telescope configuration in terms of localising signals in the sky and determining the volume of space they originate from.

Adding Cosmic Explorer to either configuration further improves sky-position accuracy, though the advantage of the misaligned L-shaped detectors persists. This detailed analysis provides valuable insights for the ongoing planning of a global gravitational-wave network, helping to optimise its capabilities for groundbreaking discoveries in the years to come.