Real-time inference for binary neutron star mergers using machine learning.

Area of Science:

• Astrophysics
• Gravitational-wave astronomy
• Multi-messenger astronomy

Background:

• Binary neutron star mergers produce both gravitational-wave (GW) and electromagnetic signals.
• The 2017 observation of GW170817 demonstrated the power of multi-messenger astronomy for discoveries in cosmology, nuclear physics, and gravity.
• Rapid analysis of GW data is crucial for coordinating time-sensitive electromagnetic observations, but current methods often involve accuracy-sacrificing approximations.

Purpose of the Study:

• To develop a machine-learning framework for rapid and accurate inference of binary neutron star merger events.
• To overcome the limitations of approximate, low-latency GW analysis methods.
• To enhance multi-messenger observations by providing precise and timely astrophysical parameters.

Main Methods:

• A novel machine-learning framework is presented for complete binary neutron star inference.
• The framework performs analysis in approximately 1 second without approximations.
• It is designed to handle complex and long GW signals.

Main Results:

• The framework provides accurate sky localization even before the merger.
• It achieves approximately 30% improved localization precision compared to approximate low-latency methods.
• Detailed information on luminosity distance, inclination, and masses is obtained, aiding in prioritizing telescope observations.

Conclusions:

• The machine-learning approach significantly enhances multi-messenger observations of binary neutron star mergers.
• Its flexibility and reduced computational cost offer new avenues for studying the equation of state of neutron stars.
• The method's scalability to long signals positions it as a blueprint for future GW detectors.