ausblenden:
Schlagwörter:
General Relativity and Quantum Cosmology, gr-qc, Astrophysics, High Energy Astrophysical Phenomena, astro-ph.HE
Zusammenfassung:
Gravitational-wave (GW) signals from coalescing compact binaries carry
enormous information about the source dynamics and are an excellent tool to
probe unknown astrophysics and fundamental physics. Though the updated catalog
of compact binary signals reports evidence for slowly spinning systems and
unequal mass binaries, the data so far cannot provide convincing proof of
strongly precessing binaries. Here, we use the GW inference library parallel
Bilby to compare the performance of two waveform models for measuring
spin-induced orbital precession. One of the waveform models incorporates both
spin-precession effects and sub-dominant harmonics. The other model accounts
for precession but only includes the leading harmonic. By simulating signals
with varying mass ratios and spins, we find that the waveform model with
sub-dominant harmonics enables us to infer the presence of precession in most
cases accurately. In contrast, the dominant model often fails to extract enough
information to measure precession. In particular, it cannot distinguish a
face-on highly precessing binary from a slowly precessing binary system
irrespective of the binary's mass ratio. As expected, we see a significant
improvement in measuring precession for edge-on binaries. Other intrinsic
parameters also become better constrained, indicating that precession effects
help break the correlations between mass and spin parameters. However, the
precession measurements are prior dominated for equal-mass binaries with
face-on orientation, even if we employ waveform model including subdominant
harmonics. In this case, doubling the signal-to-noise ratio does not help to
reduce these prior induced biases. As we expect detections of highly spinning
binary signals with misaligned spin orientations in the future, simulation
studies like ours are crucial for understanding the prospects and limitations
of GW parameter inferences.
