GW231123: extreme spins or microglitches?

TL;DR

GW231123 presents an apparent tension between its reported extreme spins and the astrophysical expectations for such a massive binary. The authors introduce a microglitch model to account for weak, nonstationary non-Gaussian noise in LIGO data, showing that microglitches can bias short-duration CBC inferences toward $χ \approx 1$; conversely, including the glitch model recovers spins near $χ \approx 0.7$ in simulations. Their background analysis finds evidence for a population of microglitches with a duty cycle around $R_g \approx 0.07$ Hz, implying that microglitches could affect a substantial fraction of events, including two-detector coincidences in catalogs. The results, though computationally expensive, motivate reanalysis of GW231123 and similar events with explicit glitch modeling to obtain robust astrophysical conclusions about black-hole spins and formation channels.

Abstract

The recently reported binary black hole merger, GW231123, has unusual properties that make it hard to explain astrophysically. Parameter estimation studies are consistent with maximally spinning black holes and the dimensionless spin of the more massive component is constrained to be $χ_1\gtrsim 0.8$. Analysis of data also revealed potential systematics that could not be fully replicated with simulated studies. We explore the possibility that these measurements are biased due to unmodeled non-Gaussian noise in the detectors, and that the actual black hole spins are more modest. We present evidence for a population of \textit{microglitches} in LIGO gravitational-wave strain data that can lead to biases in the parameter estimation of short-duration signals such as GW231123. Using simulated data of a massive event like GW231123, we demonstrate how microglitches can bias our measurements of black hole spins toward $χ\approx1$ with negligible posterior support for the true value of $χ\approx0.7$. We develop a noise model to account for microglitches and show that this model successfully reduces biases in the recovery of signal parameters. We characterize the microglitch population in real interferometer data surrounding GW231123 and find a single detector glitch duty cycle of $0.57_{-0.19}^{+0.21}$, which implies nearly a $100\%$ probability that at least one event through the fourth gravitational wave transient catalog coincides with microglitches in two detectors. We argue that further investigations are required before we can have a confident picture of the astrophysical properties of GW231123.

Figures (5)

• Figure 1: Schematic of our glitch model. The left panel shows the time series of an example microglitch, as defined in Eq. (1) with parameters $A = 5.8\times10^{-22}$, $\sigma=10^{-2}$, and $t_0 = 5.9s$. The middle panel shows a frequency-domain representation of the same glitch (orange) and a GW231123-like BBH signal (blue). The right panel shows the time series reconstruction of the frequency-domain glitch (green), the BBH (blue), and their sum (orange) after applying a bandpass filter (unlike the left panel which is not bandpassed).
• Figure 2: The left-hand panel shows the posterior distribution for the fraction of segments near GW231123 that contain a microglitch. The right-hand panel is the same plot, but converted into a rate.
• Figure 3: Posterior corner plot for a simulated GW231123-like event injected into Gaussian noise along with a microglitch. The true parameter values are marked in black. We show the distributions of chirp mass $\mathcal{M}$, mass ratio $q$, component spin magnitudes $\chi_{1,2}$, and tilts $\theta_{1,2}$, relative spin-orientation $\phi_{12}$ and orientation between the total and orbital angular momentum $\phi_{JL}$. The orange distribution shows the posterior obtained using the standard Gaussian likelihood. Note that the true value is in many panels strongly excluded from the two-sigma credible intervals. In particular, the spin parameters peak strongly at $\chi=1$. The blue distribution shows the posterior obtained when we analyze the same data with our glitch model. The posterior broadens to include the true values. We show all signal parameters except for luminosity distance which is marginalized and the fixed extrinsic parameters, namely sky position, coalescence phase, the polarization and inclination angles. For the standard Gaussian analysis we also marginalized over coalescence time.
• Figure 4: Reconstructed posterior samples of the marginalized parameters, for an injected glitch, with true values marked in blue.
• Figure 5: The left panel shows the inferred duty cycle from only Gaussian noise realizations and the right-hand panel shows the same but from the case with 10 glitch injections.