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Abstract

Mergers of binary neutron stars (BNSs) emit signals in both the
gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017
multi-messenger observation of GW170817 led to scientific discoveries across
cosmology, nuclear physics, and gravity. Central to these results were the sky
localization and distance obtained from GW data, which, in the case of
GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours
after the GW signal. Fast analysis of GW data is critical for directing
time-sensitive EM observations; however, due to challenges arising from the
length and complexity of signals, it is often necessary to make approximations
that sacrifice accuracy. Here, we present a machine learning framework that
performs complete BNS inference in just one second without making any such
approximations. Our approach enhances multi-messenger observations by providing
(i) accurate localization even before the merger; (ii) improved localization
precision by $\sim30\%$ compared to approximate low-latency methods; and (iii)
detailed information on luminosity distance, inclination, and masses, which can
be used to prioritize expensive telescope time. Additionally, the flexibility
and reduced cost of our method open new opportunities for equation-of-state
studies. Finally, we demonstrate that our method scales to extremely long
signals, up to an hour in length, thus serving as a blueprint for data analysis
for next-generation ground- and space-based detectors.