Scalable and sequential inference of the neutron star equation of state with the Einstein Telescope
Abstract
Future gravitational-wave observatories such as the Einstein Telescope will detect tens of thousands of binary neutron star mergers per year, making gravitational waves a dominant probe of the neutron-star equation of state in the coming decades. However, extracting this information requires hierarchical inference across a rapidly growing catalog of events, and existing methods must restart from scratch whenever a new event arrives, making them impractical at this scale. In this Letter, we introduce a hybrid sequential Monte Carlo algorithm that combines data and likelihood tempering to adaptively update the equation-of-state posterior in batches, reusing previous inference results instead of restarting from a wide prior. Accelerated by GPU hardware, our method infers the equation of state from a simulated month of around 1500 binary neutron star mergers in a few hours on a single GPU, and we project that a full year of detections could be processed in a few days on current hardware. This establishes sequential Monte Carlo as a practical route for real-time hierarchical inference with future gravitational-wave detectors.
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BibTeX
@misc{wouters2026scalable,
title = {{Scalable and sequential inference of the neutron star equation of state with the Einstein Telescope}},
author = {Thibeau Wouters and Thomas C. K. Ng and Hauke Koehn and Fabian Gittins and Peter T. H. Pang and Tim Dietrich and Chris Van Den Broeck},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07975},
eprint = {2610.07975},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07975},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Scalable and sequential inference of the neutron star equation of state with the Einstein Telescope
AU - Thibeau Wouters
AU - Thomas C. K. Ng
AU - Hauke Koehn
AU - Fabian Gittins
AU - Peter T. H. Pang
AU - Tim Dietrich
AU - Chris Van Den Broeck
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.07975
UR - https://arxiv.org/abs/2610.07975
AB - Future gravitational-wave observatories such as the Einstein Telescope will detect tens of thousands of binary neutron star mergers per year, making gravitational waves a dominant probe of the neutron-star equation of state in the coming decades. However, extracting this information requires hierarchical inference across a rapidly growing catalog of events, and existing methods must restart from scratch whenever a new event arrives, making them impractical at this scale. In this Letter, we introduce a hybrid sequential Monte Carlo algorithm that combines data and likelihood tempering to adaptively update the equation-of-state posterior in batches, reusing previous inference results instead of restarting from a wide prior. Accelerated by GPU hardware, our method infers the equation of state from a simulated month of around 1500 binary neutron star mergers in a few hours on a single GPU, and we project that a full year of detections could be processed in a few days on current hardware. This establishes sequential Monte Carlo as a practical route for real-time hierarchical inference with future gravitational-wave detectors.
ER -