Which Is Which? Identification of the Two Compact Objects in Gravitational-Wave Binaries

Area of Science:

• Astrophysics
• Gravitational-wave astronomy
• Machine Learning

Background:

• Gravitational-wave astronomy detects compact objects exclusively in binary systems.
• Identifying individual components (object 1 vs. object 2) is crucial but often overlooked.
• Current mass-based labeling introduces systematic uncertainties in data analysis.

Purpose of the Study:

• To develop a novel method for robustly identifying compact objects within binary systems.
• To address and mitigate labeling systematics in gravitational-wave data inference.
• To improve the interpretation of gravitational-wave signals from compact binary mergers.

Main Methods:

• Framing object identification as a constrained clustering problem, a semisupervised machine learning technique.
• Utilizing the entire posterior distribution for object labeling, rather than sample-by-sample mass assignment.
• Applying this method to analyze gravitational-wave data from compact binary coalescences.

Main Results:

• Significant improvement in the precision of black hole spin measurements (up to 50%).
• Reduction in posterior multimodalities and tails, leading to more Gaussian-like distributions.
• Facilitated identification of the nature of compact objects (black hole vs. neutron star).
• Estimated 10% of existing LIGO/Virgo posterior samples may require relabeling.

Conclusions:

• The proposed constrained clustering approach effectively resolves labeling systematics in gravitational-wave data.
• This method enhances the accuracy and reliability of compact object characterization.
• Improved data interpretation has significant implications for understanding black hole and neutron star populations.