Published May 15, 2024 | Version Published
Journal Article Open

Gravitational waves carry information beyond effective spin parameters but it is hard to extract

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon University of California, Berkeley
  • 3. ROR icon University of Chicago

Abstract

Gravitational wave observations of binary black hole mergers probe their astrophysical origins via the binary spin, namely the spin magnitudes and directions of each component black hole, together described by six degrees of freedom. However, the emitted signals primarily depend on two effective spin parameters that condense the spin degrees of freedom to those parallel and those perpendicular to the orbital plane. Given this reduction in dimensionality between the physically relevant problem and what is typically measurable, we revisit the question of whether information about the component spin magnitudes and directions can successfully be recovered via gravitational-wave observations, or if we simply extrapolate information about the distributions of effective spin parameters. To this end, we simulate three astrophysical populations with the same underlying effective-spin distribution but different spin magnitude and tilt distributions, on which we conduct full individual-event and population-level parameter estimation. We find that parametrized population models can indeed qualitatively distinguish between populations with different spin magnitude and tilt distributions at current sensitivity. However, it remains challenging to either accurately recover the true distribution or to diagnose biases due to model misspecification. We attribute the former to practical challenges of dealing with high-dimensional posterior distributions, and the latter to the fact that each individual event carries very little information about the full six spin degrees of freedom.

 

Copyright and License

© 2024 American Physical Society.

Acknowledgement

Software References

Software used: emcee [56], bilby (version 2.2.2) [51,52], dynesty (version 2.1.2) [50], numpy [77], scipy [78], matplotlib [79], seaborn [80], astropy [81,82], jax [83], numpyro [57,58].

Data Availability

Files

PhysRevD.109.104036.pdf

Files (14.8 MB)

Name Size Download all
md5:30a3bccf11f71b202409c43039ea8bca
14.8 MB Preview Download

Additional details

Identifiers

ISSN
2470-0029

Funding

National Science Foundation
PHY-2150027
National Science Foundation
PHY-2110111
National Science Foundation
PHY-2308770
Schmidt Family Foundation
Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship
National Science Foundation
PHY-0757058
National Science Foundation
PHY-0823459

Caltech Custom Metadata

Caltech groups
Astronomy Department, Walter Burke Institute for Theoretical Physics, TAPIR, LIGO