GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

GW190814 was first identified on 2019 August 14, 21:11:00 UTC as a loud two-detector event in LIGO Livingston and Virgo data (S/N 21.4 and 4.3) by the low-latency GstLAL matched-filtering search pipeline for coalescing binaries (Cannon et al. 2012; Privitera et al. 2014; Messick et al. 2017; Sachdev et al. 2019; Hanna et al. 2020). Matched-filtering searches use banks (Sathyaprakash & Dhurandhar 1991; Blanchet et al. 1995; Owen 1996; Owen & Sathyaprakash 1999; Damour et al. 2001; Blanchet et al. 2005; Cokelaer 2007; Harry et al. 2009; Brown et al. 2013; Ajith et al. 2014; Harry et al. 2014; Capano et al. 2016b; Roy et al. 2017, 2019a; Indik et al. 2018) of modeled gravitational waveforms (Buonanno & Damour 1999; Arun et al. 2009; Blanchet 2014; Bohé et al. 2017; Pürrer 2016) as filter templates. A Notice was issued through NASA's Gamma-ray Coordinates Network (GCN) 20 minutes later (LIGO Scientific Collaboration & Virgo Collaboration 2019a) with a two-detector source localization computed using the rapid Bayesian algorithm BAYESTAR (Singer & Price 2016) that is shown in Figure 2. The event was initially classified as "MassGap"(Kapadia et al. 2020; LIGO Scientific Collaboration & Virgo Collaboration 2019b), implying that at least one of the binary merger components was found to have a mass between 3 and 5 M_⊙ in the low-latency analyses.

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Other low-latency searches, including the matched-filtering based MBTA (Adams et al. 2016) and PyCBC (Nitz et al. 2017, 2018, 2019; Usman et al. 2016) pipelines, could not detect the event at the time as its S/N in Virgo data was below their single-detector detection thresholds. Test versions of MBTA and the additional matched-filtering pipeline SPIIR (Hooper et al. 2012; Liu et al. 2012; Guo et al. 2018) operating with a lower S/N threshold also identified the event with consistent attributes.

Shortly thereafter, reanalyses including LIGO Hanford data were performed using GstLAL and PyCBC. A coincident gravitational-wave signal was identified in all three detectors by both searches, with S/N 21.6 in LIGO Livingston, 10.6 in LIGO Hanford, and 4.5 in Virgo data (as measured by GstLAL, consistent with S/Ns reported by PyCBC). Results of these three-detector analyses were reported in a GCN Circular within 2.3 hr of the time of the event (LIGO Scientific Collaboration & Virgo Collaboration 2019c, 2019d), providing a three-detector localization (Singer & Price 2016) constraining the distance to 220–330 Mpc and the sky area to 38 deg² at the 90% credible level. Another GCN Circular (LIGO Scientific Collaboration & Virgo Collaboration 2019e) sent 13.5 hours after the event updated the source localization to a distance of 215–320 Mpc, the sky area to 23 deg², and the source classification to "NSBH" (Kapadia et al. 2020; LIGO Scientific Collaboration & Virgo Collaboration 2019b), indicating that the secondary had a mass below 3 M_⊙. These updated sky localizations are also shown in Figure 2. The two disjoint sky localizations arise because the low S/N in the Virgo detector (4.5) means that the data are consistent with two different signal arrival times in that detector.

Several external groups performed multimessenger follow-up of the source with observations across the electromagnetic spectrum (e.g., Dobie et al. 2019; Gomez et al. 2019; Lipunov et al. 2019; Ackley et al. 2020; Antier et al. 2020; Andreoni et al. 2020; Watson et al. 2020; Vieira et al. 2020) and with neutrino observations (e.g., Ageron et al. 2019; The IceCube Collaboration 2019). No counterpart candidates were reported. The nondetection is consistent with the source's highly unequal mass ratio and low primary spin (LIGO Scientific Collaboration & Virgo Collaboration 2019d, 2019e; Fernández et al. 2020; Morgan et al. 2020). Tentative constraints placed by multimessenger studies on the properties of the system, such as the ejecta mass and maximum primary spin (Ackley et al. 2020; Andreoni et al. 2020; Coughlin et al. 2020; Kawaguchi et al. 2020) or the circum-merger density (Dobie et al. 2019) assuming a neutron star–black hole (NSBH) source, may need to be revisited in light of the updated source parameters we present in Section 4.1.

The significance of GW190814 was estimated by follow-up searches using improved calibration and refined data-quality information that are not available in low latency. They also used longer stretches of data for better precision (Abbott et al. 2016b, 2016c). With LIGO Hanford data being usable but not in nominal observing mode at the time of GW190814, we used only data from the LIGO Livingston and Virgo detectors for significance estimation. GW190814 was identified as a confident detection in analyses of detector data collected over the period from 2019 August 7 to August 15 by the two independent matched-filtering searches GstLAL and PyCBC, with S/N values consistent with the low-latency analyses. The production version of PyCBC for O3 estimates significance only for events that are coincident in the LIGO Hanford and LIGO Livingston detectors, and therefore an extended version (Davies et al. 2020) was used for GW190814 in order to enable the use of Virgo data in significance estimation.

GstLAL and PyCBC use different techniques for estimating the noise background and methods of ranking gravitational-wave candidates. Both use results from searches over non-time-coincident data to improve their noise background estimation (Privitera et al. 2014; Messick et al. 2017; Usman et al. 2016). Using data from the first six months of O3 and including all events during this period in the estimation of noise background, GstLAL estimated a false-alarm rate (FAR) of 1 in 1.3 × 10³ yr for GW190814. Using data from the 8 day period surrounding GW190814 and including this and all quieter events during this period in noise background estimation, the extended PyCBC pipeline (Davies et al. 2020) estimated an FAR for the event of 1 in 8.1 yr. The higher FAR estimate from PyCBC can be attributed to the event being identified by the pipeline as being quieter than multiple noise events in Virgo data. As PyCBC estimates background statistics using noncoincident data from both detectors, these louder noise events in Virgo data can form chance coincidences with the signal in LIGO Livingston data and elevate the noise background estimate for coincident events, especially when considering shorter data periods. All estimated background events that were ranked higher than GW190814 by PyCBC were indeed confirmed to be coincidences of the candidate event itself in LIGO Livingston with random noise events in Virgo. The stated background estimates are therefore conservative (Capano et al. 2016a). We also estimate the background excluding the candidate from the calculation, a procedure that yields a mean-unbiased estimation of the distribution of noise events (Abbott et al. 2016d; Capano et al. 2016a). In this case, with GstLAL we found an FAR of <1 in 10⁵ yr while with PyCBC we found an FAR of <1 in 4.2 × 10⁴ yr. With both pipelines identifying GW190814 as more significant than any event in the background, the FARs assigned are upper bounds.

When data from LIGO Hanford were included, GW190814 was also identified by the unmodeled coherent WaveBurst (cWB) search that targets generic gravitational-wave transients with increasing frequency over time without relying on waveform models (Klimenko et al. 2008, 2016; Abbott et al. 2016e). We found an FAR of <1 in 10³ yr of observing time against the noise background from LIGO Hanford and LIGO Livingston data, consistent with the other searches.

From the ∼300 observed cycles above 20 Hz, we are able to tightly constrain the source properties of GW190814. Our analysis shows that GW190814's source is a binary with an unequal mass ratio q = ${0.112}_{-0.009}^{+0.008}$, with individual source masses m₁ = ${23.2}_{-1.0}^{+1.1}$ M_⊙ and m₂ = ${2.59}_{-0.09}^{+0.08}$ M_⊙, as shown in Figure 3. A summary of the inferred source properties is given in Table 1. We assume a standard flat ΛCDM cosmology with Hubble constant H₀ = 67.9 km s⁻¹ Mpc⁻¹ (Ade et al. 2016).

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Table 1.  Source Properties of GW190814: We Report the Median Values Along with the Symmetric 90% Credible Intervals for the SEOBNRv4PHM (EOBNR PHM) and IMRPhenomPv3HM (Phenom PHM) Waveform Models

	EOBNR PHM	Phenom PHM	Combined
Primary mass m1/M⊙	${23.2}_{-0.9}^{+1.0}$	${23.2}_{-1.1}^{+1.3}$	${23.2}_{-1.0}^{+1.1}$
Secondary mass m2/M⊙	${2.59}_{-0.08}^{+0.08}$	${2.58}_{-0.10}^{+0.09}$	${2.59}_{-0.09}^{+0.08}$
Mass ratio q	${0.112}_{-0.008}^{+0.008}$	${0.111}_{-0.010}^{+0.009}$	${0.112}_{-0.009}^{+0.008}$
Chirp mass ${ \mathcal M }/{M}_{\odot }$	${6.10}_{-0.05}^{+0.06}$	${6.08}_{-0.05}^{+0.06}$	${6.09}_{-0.06}^{+0.06}$
Total mass M/M⊙	${25.8}_{-0.8}^{+0.9}$	${25.8}_{-1.0}^{+1.2}$	${25.8}_{-0.9}^{+1.0}$
Final mass Mf/M⊙	${25.6}_{-0.8}^{+1.0}$	${25.5}_{-1.0}^{+1.2}$	${25.6}_{-0.9}^{+1.1}$
Upper bound on primary spin magnitude χ1	0.06	0.08	0.07
Effective inspiral spin parameter χeff	${0.001}_{-0.056}^{+0.059}$	$-{0.005}_{-0.065}^{+0.061}$	$-{0.002}_{-0.061}^{+0.060}$
Upper bound on effective precession parameter χp	0.07	0.07	0.07
Final spin χf	${0.28}_{-0.02}^{+0.02}$	${0.28}_{-0.03}^{+0.02}$	${0.28}_{-0.02}^{+0.02}$
Luminosity distance DL/Mpc	${235}_{-45}^{+40}$	${249}_{-43}^{+39}$	${241}_{-45}^{+41}$
Source redshift z	${0.051}_{-0.009}^{+0.008}$	${0.054}_{-0.009}^{+0.008}$	${0.053}_{-0.010}^{+0.009}$
Inclination angle Θ/rad	${0.9}_{-0.2}^{+0.3}$	${0.8}_{-0.2}^{+0.2}$	${0.8}_{-0.2}^{+0.3}$
Signal-to-noise ratio in LIGO HanfordρH	${10.6}_{-0.1}^{+0.1}$	${10.7}_{-0.2}^{+0.1}$	${10.7}_{-0.2}^{+0.1}$
Signal-to-noise ratio in LIGO LivingstonρL	${22.21}_{-0.15}^{+0.09}$	${22.16}_{-0.17}^{+0.09}$	${22.18}_{-0.17}^{+0.10}$
Signal-to-noise ratio in VirgoρV	${4.3}_{-0.5}^{+0.2}$	${4.1}_{-0.6}^{+0.2}$	${4.2}_{-0.6}^{+0.2}$
Network Signal-to-noise ratio ρHLV	${25.0}_{-0.2}^{+0.1}$	${24.9}_{-0.2}^{+0.1}$	${25.0}_{-0.2}^{+0.1}$

Note. The primary spin magnitude and the effective precession is given as the 90% upper limit. The inclination angle is folded to [0, π/2]. The last column is the result of combining the posteriors of each model with equal weight. The sky location of GW190814 is shown in Figure 2.

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We report detailed results obtained from the two precessing BBH signal models including subdominant multipole moments: Phenom PHM and EOBNR PHM. In order to compare the template models, we compute their Bayes factor (${\mathrm{log}}_{10}{ \mathcal B }$). We find no significant evidence that one waveform family is preferred over the other as the Bayes factor between Phenom PHM and EOBNR PHM is ${\mathrm{log}}_{10}{ \mathcal B }\simeq 1.0$. As a result, we combine the posterior samples with equal weight, in effect marginalizing over a discrete set of signal models with a uniform probability. This is shown in the last column of Table 1, and we refer to these values throughout the paper unless stated otherwise.

We find that the secondary mass lies in the range of 2.50–2.67 M_⊙. This inferred secondary mass exceeds the bounds of the primary component in GW190425 (1.61–2.52 M_⊙; Abbott et al. 2020a) and the most massive known pulsar in the Galaxy: ${2.14}_{-0.09}^{+0.10}\,{M}_{\odot }$ at 68.3% credible interval (Cromartie et al. 2019). Furthermore, the secondary is more massive than bounds on the maximum NS mass from studies of the remnant of GW170817, and from theoretical (Abbott et al. 2018) and observational estimates (Farr & Chatziioannou 2020). The inferred secondary mass is comparable to the putative BH remnant mass of GW170817 (Abbott et al. 2019b).

The primary object is identified as a BH based on its measured mass of ${23.2}_{-1.0}^{+1.1}$ M_⊙. Due to accurately observing the frequency evolution over a long inspiral, the chirp mass is well constrained to ${6.09}_{-0.06}^{+0.06}$ M_⊙. The inferred mass ratio q = ${0.112}_{-0.009}^{+0.008}$ makes GW190814 only the second gravitational-wave observation with a significantly unequal mass ratio (Abbott et al. 2019a, 2020d).

Given that this system is in a region of the parameter space that has not been explored via gravitational-wave emission previously, we test possible waveform systematics by comparing the Phenom and EOB waveform families. Differences in the inferred secondary mass are shown in Figure 4. The results indicate that the inferred secondary mass is robust to possible waveform systematics, with good agreement between the Phenom PHM and EOBNR PHM signal models. Signal models that exclude higher multipoles or precession do not constrain the secondary mass as well.

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The time delay of a signal across a network of gravitational wave detectors, together with the relative amplitude and phase at each detector, allows us to measure the location of the GW source on the sky (Abbott et al. 2020b). We localize GW190814's source to within 18.5 deg² at 90% probability, as shown in Figure 2. This is comparable to the localization of GW170817 (Abbott et al. 2017a, 2019a).

Spins are a fundamental property of BHs. Their magnitude and orientation carry information regarding the evolution history of the binary. The effective inspiral spin parameter χ_eff (Damour 2001; Racine 2008; Santamaría et al. 2010; Ajith et al. 2011) contains information about the spin components that are perpendicular to the orbital plane. We infer that χ_eff = $-{0.002}_{-0.061}^{+0.060}$. The tight constraints are consistent with being able to measure the phase evolution from the long inspiral.

Orbital precession occurs when there is a significant spin component in the orbital plane of the binary (Apostolatos et al. 1994). We parameterize precession by the effective precession spin parameter 0 ≤ χ_p ≤ 1 (Schmidt et al. 2015). This effect is difficult to measure for face-on and face-off systems (Apostolatos et al. 1994; Buonanno et al. 2003; Vitale et al. 2014, 2017; Fairhurst et al. 2019a, 2019b). GW190814 constrains the inclination of the binary to be Θ = ${0.8}_{-0.2}^{+0.3}$ rad. Since the system is neither face-on nor face-off, we are able to put strong constraints on the precession of the system: χ_p = ${0.04}_{-0.03}^{+0.04}$. This is both the strongest constraint on the amount of precession for any gravitational-wave detection to date, and the first gravitational-wave measurement that conclusively measures near-zero precession (Abbott et al. 2019a, 2020a, 2020d).

By computing the Bayes factor between a precessing and nonprecessing signal model (${\mathrm{log}}_{10}{ \mathcal B }\sim 0.5$ in favor of precession), we find inconclusive evidence for in-plane spin. This is consistent with the inferred power from precession S/N ρ_p (Fairhurst et al. 2019a, 2019b), whose recovered distribution resembles that expected in the absence of any precession in the signal; see Figure 5. The ρ_p calculation assumes a signal dominated by the ℓ = 2 mode; however, we have verified that the contribution of higher harmonics to the measurement of spin precession is subdominant by a factor of 5. The data are therefore consistent with the signal from a nonprecessing system.

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Figure 4 shows that signal models including spin-precession effects give tighter constraints on the secondary mass compared to their nonprecessing equivalents. Signal models that include spin-precession effects can constrain χ_p, whereas nonprecessing signal models cannot provide information on in-plane spin components. In all analyses, we assume a prior equivalent to spin orientations being isotropically distributed. We find that the data are inconsistent with large χ_p and consistent with any secondary spin. Therefore, for precessing signal models the allowed q–χ_eff parameter space is restricted, which helps to break the degeneracy (Poisson & Will 1995; Baird et al. 2013; Farr et al. 2016; Ng et al. 2018). Consequently, the extra information from constraining χ_p to small values enables a more precise measurement of the secondary mass.

The asymmetry in the masses of GW190814 means that the spin of the more massive object dominates contributions to χ_eff and χ_p. As both χ_eff and χ_p are tightly constrained, we are able to bound the primary spin of GW190814 to be χ₁ ≤ 0.07, as shown in Figure 6. This is the strongest constraint on the primary spin for any gravitational-wave event to date (Abbott et al. 2019a, 2020a, 2020d).

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The joint posterior probability of the magnitude and orientation of χ₁ and χ₂ are shown in Figure 6. Deviations from uniform shading indicate a spin property measurement. The primary spin is tightly constrained to small magnitudes, but its orientation is indistinguishable from the prior distribution. The spin of the less massive object, χ₂, remains unconstrained; the posterior distribution is broadly consistent with the prior.

The final mass M_f and final dimensionless spin χ_f of the merger remnant are estimated under the assumption that the secondary is a BH. By averaging several fits calibrated to numerical relativity (Hofmann et al. 2016; Johnson-McDaniel et al. 2016; Healy & Lousto 2017; Jiménez-Forteza et al. 2017), we infer the final mass and spin of the remnant BH to be ${25.6}_{-0.9}^{+1.1}$ M_⊙ and ${0.28}_{-0.02}^{+0.02}$, respectively. The final spin is lower than for previous mergers (Abbott et al. 2019a, 2020d), as expected from the low primary spin and smaller orbital contribution due to the asymmetric masses.

The relative importance of a subdominant multipole moment increases with mass ratio. Each subdominant multipole moment has a different angular dependence on the emission direction. With significant evidence for multipoles other than the dominant (ℓ, m) = (2, 2) quadrupole, we gain an independent measurement of the inclination of the source. This allows for the distance-inclination degeneracy to be broken (Cutler & Flanagan 1994; Abbott et al. 2016f; Kalaghatgi et al. 2020; Usman et al. 2019). Measuring higher-order multipoles therefore gives more precise measurements of source parameters (van den Broeck & Sengupta 2007a, 2007b; Kidder 2008; Blanchet et al. 2008; Mishra et al. 2016; Kumar et al. 2019).

GW190412 was the first event where there was significant evidence for higher-order multipoles (Kumar et al. 2019; Payne et al. 2019; Abbott et al. 2020d). GW190814 exhibits stronger evidence for higher-order multipoles, with ${\mathrm{log}}_{10}{ \mathcal B }\simeq 9.6$ in favor of a higher-multipole versus a pure quadrupole model. The (ℓ, m) = (3, 3) is the strongest subdominant multipole, with ${\mathrm{log}}_{10}{ \mathcal B }\simeq 9.1$ in favor of a signal model including both the (ℓ, m) = (2, 2) and (3, 3) multipole moments. GW190814's stronger evidence for higher multipoles is expected given its more asymmetric masses and the larger network S/N.

The orthogonal optimal S/N of a subdominant multipole is calculated by decomposing each multipole into components parallel and perpendicular to the dominant harmonic (Abbott et al. 2020d; Mills & Fairhurst 2020). We infer that the orthogonal optimal S/N of the (ℓ, m) = (3, 3) multipole is ${6.6}_{-1.4}^{+1.3}$, as shown in Figure 5. This is the strongest evidence for measuring a subdominant multipole to date (Kumar et al. 2019; Payne et al. 2019; Abbott et al. 2020d).

Finally, we perform two complementary analyses involving time–frequency tracks in the data to provide further evidence for the presence of higher multipoles in the signal. In the first approach (also outlined in Abbott et al. 2020d, Section 4) we predict the time–frequency track of the dominant (2, 2) multipole in the LIGO Livingston detector (as seen in Figure 1, middle panel) from an EOBNR HM parameter estimation analysis. This analysis collects energies along a time–frequency track that is α × f₂₂(t), the (2, 2) multipole's instantaneous frequency, where α is a dimensionless parameter (Roy et al. 2019b; Abbott et al. 2020d). We find prominent peaks in Y(α), the energy in the pixels along the α-th track defined in Abbott et al. (2020d), at α = 1 and 1.5, as can be seen from the on-source curve in the top panel of Figure 7. These peaks correspond to the m = 2 and m = 3 multipole predictions in the data containing the signal (on-source data). We also compute a detection statistic β (Roy et al. 2019b) of 10.09 for the presence of the m = 3 multipole with a p-value of <2.5 × 10⁻⁴, compared to a background distribution estimated over 18 hr of data adjacent to the event (off-source data), where the largest background β is 7.59. The significant difference between on- and off-source values provides much stronger evidence for the presence of higher multipoles than what is reported for GW190412 (Abbott et al. 2020d).

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The second analysis uses waveform-agnostic methods to reconstruct the signal. It then compares the observed coherent signal energy in the LIGO Hanford–LIGO Livingston–Virgo network of detectors, as identified by the cWB detection pipeline (Klimenko et al. 2016), with the predictions of a waveform model without higher multipoles (EOBNR; Prodi et al. 2020) to investigate if the description of the underlying signal is incomplete if we do not include contributions from the m = 3 multipole in our waveform model. We compute a test statistic, the squared sum of the coherent residuals estimated over selected time–frequency tracks parameterized in terms of the same α parameter defined in the previous analysis (Roy et al. 2019b; Abbott et al. 2020d). Each time–frequency track centered on α includes frequencies within [α − 0.1, α + 0.1] × f₂₂(t), and times within [t_merger − 0.5 s, t_merger − 0.03 s], where f₂₂(t) and t_merger correspond to the maximum likelihood template from the EOBNR parameter estimation analysis. We further compute a background distribution using simulated signals in off-source data (Prodi et al. 2020), and compute p-values for the on-source results as a function of α (Figure 7, bottom panel). We find a minimum p–value of 6.8 × 10⁻³ at α = 1.5, providing strong evidence that the disagreement between the actual event and the EOBNR prediction is because of the absence of the m = 3 multipoles in the waveform model. The local minimum near α = 2 is not an indication of the m = 4 multipoles, but rather a statistical fluctuation that is consistent with similar behavior seen for studies with simulated signals described in detail in Prodi et al. (2020).

Although the two time–frequency analyses are similar in motivation, the latter differs from the former in that it is not restricted to data from just one detector, but rather uses the coherent signal energy across the three-detector network. Both analyses point to strong evidence for the presence of higher multipoles in the signal.

Given the unprecedented combination of component masses found in GW190814, we take the system to represent a new class of compact binary mergers, and use our analysis of its source properties to estimate a merger rate density for GW190814-like events. Following a method described in Kim et al. (2003), we calculate a simple, single-event rate density estimate ${ \mathcal R }$ according to our sensitivity to a population of systems drawn from the parameter-estimation posteriors. As in Abbott et al. (2020a), we calculate our surveyed spacetime volume $\langle {VT}\rangle$ semianalytically, imposing single-detector and network S/N thresholds of 5 and 10, respectively (Tiwari 2018). The semianalytic $\langle {VT}\rangle$ for GW190814 is then multiplied by a calibration factor to match results from the search pipelines assuming a once-per-century FAR threshold. The sensitivity of a search pipeline is estimated using a set of simulated signals. For computational efficiency, this was done using preexisting search pipeline simulations and the mass properties were not highly optimized. However, given that we are estimating a rate based on a single source, the calibration errors are much smaller than the statistical errors associated with the estimate. The simulated sources were uniformly distributed in comoving volume, component masses, and component spins aligned with the orbital angular momentum. For O1 and O2, the simulated BH mass range was 5–100 M_⊙, but for the first part of O3 we are analyzing here, the injected range was 2.5–40 M_⊙ (following our updated knowledge of the BH mass distribution); the NS mass range was 1–3 M_⊙, and component spins are <0.95. As GW190814 occurred when LIGO Hanford was not in nominal observing mode, it is not included in the production PyCBC results, and we use GstLAL results to calculate the merger rate.

We assume a Poisson likelihood over the astrophysical rate with a single count and we apply a Jeffreys ${{ \mathcal R }}^{-1/2}$ prior to obtain rate posteriors. The analysis was done using samples from the Phenom PHM posterior and separately from the EOBNR PHM posterior, producing the same result in both cases. We find the merger rate density of GW190814-like systems to be ${7}_{-6}^{+16}\,{\mathrm{Gpc}}^{-3}\,{\mathrm{yr}}^{-1}$.

As a consistency check, we used the PyCBC search results to calculate an upper limit. Repeating the rate calculation with a PyCBC-based $\langle {VT}\rangle$ calibration and zero event count, we obtain an upper limit consistent (to within 10%) with the upper limit of the merger rate estimated using GstLAL search results. We conclude that the uncertainty in our estimate of the rate density for the class of mergers represented by GW190814 is primarily dominated by Poisson statistics.

The primary mass measurement of ${23.2}_{-1.0}^{+1.1}$ M_⊙ securely identifies the heavier component of GW190814 as a BH, but the secondary mass of ${2.59}_{-0.09}^{+0.08}$ M_⊙ may be compatible with either an NS or a BH depending on the maximum mass supported by the unknown NS equation of state (EOS). The source's asymmetric masses, the nondetection of an electromagnetic counterpart and the lack of a clear signature of tides or spin-induced quadrupole effects in the waveform do not allow us to distinguish between a BBH or an NSBH. Instead, we rely on comparisons between m₂ and different estimates of the maximum NS mass, M_max, to indicate the source classification preferred by data: if m₂ > M_max, then the NSBH scenario is untenable.

While some candidate EOSs from nuclear theory can support nonrotating NSs with masses of up to ∼3 M_⊙ (e.g., Müller & Serot 1996), such large values of M_max are disfavored by the relatively small tidal deformabilities measured in GW170817 (Abbott et al. 2017a, 2019b), which correlate with smaller internal pressure gradients as a function of density and hence a lower threshold for gravitational collapse. By adopting a phenomenological model for the EOS, conditioning it on GW170817, and extrapolating the constraints to the high densities relevant for the maximum mass, Lim & Holt (2019) and Essick et al. (2020) place M_max ≲ 2.3 M_⊙. Similarly, the EOS inference reported in Abbott et al. (2018), based on an analysis of GW170817 with a spectral parameterization (Lindblom 2010; Lindblom & Indik 2012, 2014) for the EOS, implies a 90% credible upper bound of M_max ≤ 2.43 M_⊙, with tenuous but nonzero posterior support beyond 2.6 M_⊙. We calculate the corresponding M_max posterior distribution, shown in the right panel of Figure 3, from the GW170817-informed spectral EOS samples used in Abbott et al. (2018) by reconstructing each EOS from its parameters and computing its maximum mass. Comparison with the m₂ posterior suggests that the secondary component of GW190814 is probably more massive than this prediction for M_max: the posterior probability of m₂ ≤ M_max, marginalized over the uncertainty in m₂ and M_max, is only 3%. Nevertheless, the maximum mass predictions from these kinds of EOS inferences come with important caveats: their extrapolations are sensitive to the phenomenological model assumed for the EOS; they use hard M_max thresholds on the EOS prior to account for the existence of the heaviest Galactic pulsars, which is known to bias the inferred maximum mass distribution toward the threshold (Miller et al. 2020); and they predate the NICER observatory's recent simultaneous mass and radius measurement for J0030+0451, which may increase the M_max estimates by a few percent (Landry et al. 2020) because it favors slightly stiffer EOSs than GW170817 (Miller et al. 2019; Raaijmakers et al. 2019; Riley et al. 2019; Jiang et al. 2020).

NS mass measurements also inform bounds on M_max independently of EOS assumptions. Fitting the known population of NSs in binaries to a double-Gaussian mass distribution with a high-mass cutoff, Alsing et al. (2018) obtained an empirical constraint of M_max ≤ 2.6 M_⊙ (one-sided 90% confidence interval). Farr & Chatziioannou (2020) recently updated this analysis to include PSR J0740+6620 (Cromartie et al. 2019), which had not been discovered at the time of the original study. Based on samples from the Farr & Chatziioannou (2020) maximum-mass posterior distribution, which is plotted in the right panel of Figure 3, we find ${M}_{\max }={2.25}_{-0.26}^{+0.81}$ M_⊙. In this case, the posterior probability of m₂ ≤ M_max is 29%, again favoring the m₂ > M_max scenario, albeit less strongly because of the distribution's long tail up to ∼3 M_⊙. However, the empirical M_max prediction is sensitive to selection effects that could potentially bias it (Alsing et al. 2018). In particular, masses are only measurable for binary pulsars, and the mass distribution of isolated NSs could be different. Additionally, the discovery of GW190425 (Abbott et al. 2020a) should also be taken into account in the population when predicting M_max.

Finally, the NS maximum mass is constrained by studies of the merger remnant of GW170817. Although no postmerger gravitational waves were observed (Abbott et al. 2017g, 2019f), modeling of the associated kilonova (Abbott et al. 2017b, 2017d; Cowperthwaite et al. 2017; Kasen et al. 2017; Villar et al. 2017) suggests that the merger remnant collapsed to a BH after a brief supramassive or hypermassive NS phase during which it was stabilized by uniform or differential rotation. Assuming this ultimate fate for the merger remnant immediately implies that no NS can be stable above ∼2.7 M_⊙, but it places a more stringent constraint on NSs that are not rotationally supported. The precise mapping from the collapse threshold mass of the remnant to M_max depends on the EOS, but by developing approximate prescriptions based on sequences of rapidly rotating stars for a range of candidate EOSs, M_max has been bounded below approximately 2.2–2.3 M_⊙ (Margalit & Metzger 2017; Rezzolla et al. 2018; Ruiz et al. 2018; Shibata et al. 2019; Abbott et al. 2020c). Although the degree of EOS uncertainty in these results is difficult to quantify precisely, if we take the more conservative 2.3 M_⊙ bound at face value, then m₂ is almost certainly not an NS: the m₂ posterior distribution has negligible support below 2.3 M_⊙.

Overall, these considerations suggest that GW190814 is probably not the product of an NSBH coalescence, despite its preliminary classification as such. Nonetheless, the possibility that the secondary component is an NS cannot be completely discounted due to the current uncertainty in M_max.

There are two further caveats to this assessment. First, because the secondary's spin is unconstrained, it could conceivably be rotating rapidly enough for m₂ to exceed M_max without triggering gravitational collapse: rapid uniform rotation can stabilize a star up to ∼20% more massive than the nonrotating maximum mass (Cook et al. 1994), in which case only the absolute upper bound of ∼2.7 M_⊙ is relevant. However, it is very unlikely that an NSBH system could merge before dissipating such extreme natal NS spin angular momentum.

Second, our discussion has thus far neglected the possibility that the secondary component is an exotic compact object, such as a boson star (Kaup 1968) or a gravastar (Mazur & Mottola 2004), instead of an NS or a BH. Depending on the model, some exotic compact objects can potentially support masses up to and beyond 2.6 M_⊙ (Cardoso & Pani 2019). Our analysis does not exclude this hypothesis for the secondary.

Since the NSBH scenario cannot be definitively ruled out, we examine GW190814's potential implications for the NS EOS, assuming that the secondary proves to be an NS. This would require M_max to be no less than m₂, a condition that severely constrains the distribution of EOSs compatible with existing astrophysical data. The combined constraints on the EOS from GW170817 and this hypothetical maximum mass information are shown in Figure 8. Specifically, we have taken the spectral EOS distribution conditioned on GW170817 from Abbott et al. (2018) and reweighted each EOS by the probability that its maximum mass is at least as large as m₂. The updated posterior favors stiffer EOSs, which translates to larger radii for NSs of a given mass. The corresponding constraints on the radius and tidal deformability of a canonical 1.4 M_⊙ NS are R_1.4 = ${12.9}_{-0.7}^{+0.8}$ km and Λ_1.4 = ${616}_{-158}^{+273}$.

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The source of GW190814 represents a previously undetected class of coalescences that has the potential to shed light on the formation of merging compact-object binaries with highly asymmetric masses.

Electromagnetic observations of Galactic NSs and stellar-mass BHs suggest a dearth of compact objects in the ∼2.5 M_⊙ to 5 M_⊙ range (Bailyn et al. 1998; Özel et al. 2010; Farr et al. 2011; Özel et al. 2012). Observations of a few candidates with masses in this range seem to disfavor the existence of a gap (Freire et al. 2008; Neustroev et al. 2014; Giesers et al. 2018; Thompson et al. 2019; Wyrzykowski & Mandel 2020), but whether the mass gap is physical or caused by selection biases is still a matter of debate (e.g., Kreidberg et al. 2012).

From a theoretical point of view, accurately calculating the masses of compact remnants at formation is challenging, because it depends on the complex physics of the supernova explosion and the details of stellar evolution, especially for the late evolutionary stages of massive stars (Janka 2012; Müller 2016; Burrows et al. 2018, 2019). Whether the models favor the presence of a gap or a smooth transition between NSs and BHs is still unclear, and in fact some models have been developed with the purpose of reproducing this lower mass gap (Ugliano et al. 2012; Fryer et al. 2012; Kochanek 2014; Sukhbold & Woosley 2014; Ertl et al. 2016). Therefore, our robust discovery of an object with a well-constrained mass in this regime may provide crucial constraints on compact-object formation models. In fact, GW190814 demonstrates the need to adjust remnant mass prescriptions previously designed to produce a perceived mass gap. The combination of mass ratio and component masses challenges most results obtained from population synthesis simulations for isolated binaries (Dominik et al. 2012, 2015; Marchant et al. 2017; Giacobbo & Mapelli 2018; Kruckow et al. 2018; Mapelli & Giacobbo 2018; Mapelli et al. 2019; Neijssel et al. 2019; Spera et al. 2019; Olejak et al. 2020).

Population synthesis models distinguish between NSs and BHs using only a mass threshold, which is generally in the range of 2–3 M_⊙. Thus, depending on the adopted threshold and on the adopted supernova explosion model, a GW190814-like event may be labeled as either an NSBH merger or a BBH merger. Most BBH mergers have q > 0.5, while the distributions of merging NSBH binaries suggest that systems with q ≲ 0.1 may be up to ∼10³ times less common than more symmetric ones (q > 0.1) and that the mass-ratio distribution peaks at q ≈ 0.2. Furthermore, models tend to favor mergers of massive (≳1.3 M_⊙) NSs with relatively small BHs (≲15 M_⊙) in environments with subsolar metallicity (Z ≲ 0.5 Z_⊙). The tendency to disfavor mergers with highly asymmetric masses in isolated binaries may be the consequence of mass transfer (e.g., Postnov & Yungelson 2014) and common envelope episodes (e.g., Ivanova et al. 2013) that cause systems with initially asymmetric masses to evolve toward more symmetric configurations. Overall, producing mergers with such unequal masses, with a secondary in the perceived mass gap, and at the rate implied by this discovery is a challenge for current models.

Nevertheless, particular choices of poorly constrained assumptions within rapid population synthesis models may increase the number of mergers with q ≲ 0.1 so that the latter may be only a few times less common than (or even comparable to) systems with q ≃ 0.2 (e.g., Eldridge & Stanway 2016; Eldridge et al. 2017; Giacobbo & Mapelli 2018).

Another possibility is that GW190814 is of dynamical origin. Dynamical exchanges in dense stellar environments tend to pair up massive compact objects with similar masses (e.g., Sigurdsson & Hernquist 1993). This process is effective for globular clusters, where compact-object binaries may undergo tens of exchanges before they get ejected from the cluster (Portegies Zwart & McMillan 2000; Rodriguez et al. 2016, 2019; Askar et al. 2017; Park et al. 2017). For such environments, models predict that most merging BBHs have q ≃ 1 (e.g., Rodriguez et al. 2016), and the formation of NSBH binaries is highly suppressed because BHs dynamically dominate the cores over the complete lifetime of the clusters, preventing the interactions between BHs and NSs, with the consequence that the merger rate of NSBH binaries in globular clusters in the local universe is ∼10⁻²–10⁻¹ Gpc⁻³ yr⁻¹ (Clausen et al. 2013; Arca Sedda 2020; Ye et al. 2020). The rate for GW190814-like events, with a secondary in the perceived mass gap, is likely even lower. In contrast, the NSBH merger rate may be significantly higher in young star clusters (e.g., Ziosi et al. 2014) and the latter can effectively increase the number of progenitors leading to merging compact-object binaries with q ≲ 0.15 (di Carlo et al. 2019; Rastello et al. 2020). Thus, young star clusters may be promising hosts for GW190814-like events, but the parameter space relevant for GW190814 is mostly unexplored in the context of star clusters.

In dense stellar environments, GW190814-like systems may also form from a low-mass merger remnant that acquires a BH companion via dynamical interactions (Gupta et al. 2020). Gupta et al. (2020) predicts a population of second-generation BHs in the 2.2–3.8 M_⊙ range, with a peak in the distribution at 2.6 M_⊙, assuming a double-Gaussian mass distribution for the NSs. However, recent dynamical simulations of globular clusters (e.g., Ye et al. 2020) find the subsequent merger of such a second-generation BH with a larger stellar-mass BH to be exceedingly rare. A high component spin could be a distinguishing feature of a second-generation compact object, but the uninformative spin posterior for the lighter component of GW190814 provides no evidence for or against this hypothesis.

A GW190814-like merger may also have originated from a hierarchical triple in the field (e.g., Antonini et al. 2017; Silsbee & Tremaine 2017; Fragione & Loeb 2019), from a wide hierarchical quadruple system (Safarzadeh et al. 2020), or from hierarchical triples in galactic centers, where the tertiary body is a supermassive BH (Antonini & Perets 2012; Petrovich & Antonini 2017; Hoang et al. 2018; Fragione et al. 2019; Stephan et al. 2019). Specifically, Safarzadeh et al. (2020) explore the possibility that a second-generation remnant with mass 3 M_⊙ may merge with a 30 M_⊙ BH, catalyzed by a 50 M_⊙-BH perturber. The mass-ratio distributions of BBH and NSBH mergers from hierarchical systems are similar to those of field binaries and it is unclear whether hierarchies may enhance the formation of merging compact-object binaries with highly asymmetric masses (e.g., Silsbee & Tremaine 2017).

Disks of gas around supermassive BHs in active galactic nuclei may be promising environments for the formation of GW190814-like systems. For such environments, theoretical models show that merging compact-object binaries with asymmetric masses are likely, but cannot necessarily accommodate masses as low as the secondary mass of GW190814 (e.g., Yang et al. 2019). However, McKernan et al. (2020) show that the median mass ratio of NSBH mergers in active galactic nucleus disks may be as low as ∼0.07.

We conclude that the combination of masses, mass ratio, and inferred rate of GW190814 is challenging to explain, but potentially consistent with multiple formation scenarios. However, it is not possible to assess the validity of models that produce the right properties but do not make quantitative predictions about formation rates, even at some order-of-magnitude level.

Young star clusters and active galactic nucleus disks seem to be more promising hosts for GW190814-like mergers, since both these environments may enhance the formation of either progenitors of or directly merging compact-object binaries with more asymmetric masses to relevant rates. In contrast, globular-cluster models provide more robust predictions, showing that GW190814-like mergers with such asymmetric masses are outliers in the population predictions, even though a revision of the remnant-mass prescription is still needed. Isolated binaries could prove possible progenitors provided similar revisions are implemented. The importance of field multiples remains to be fully explored. Future gravitational-wave observations will provide further insights into the dominance of different channels.

Luminosity distances inferred directly from observed gravitational-wave events can be used with measurements of source redshifts in the electromagnetic spectrum to constrain cosmological parameters (Schutz 1986). Redshifts can be either obtained directly from counterparts to the gravitational-wave source (Holz & Hughes 2005), as was the case for GW170817 (Abbott et al. 2017a, 2017b, 2017e), by cross-correlation of the gravitational-wave localization posterior with catalogs of galaxy redshifts (del Pozzo 2012; Chen et al. 2018; Nair et al. 2018; Abbott et al. 2019c; Gray et al. 2020; Fishbach et al. 2019; Soares-Santos et al. 2019), by exploiting information in the neutron star equation of state (Messenger & Read 2012), or by using the redshifted masses inferred from the gravitational wave observation and assumptions about the mass distribution of the sources (Chernoff & Finn 1993; Taylor et al. 2012; Taylor & Gair 2012; Farr et al. 2019). At current sensitivities, the cosmological parameter to which LIGO–Virgo observations are most sensitive is the Hubble constant, H₀. The gravitational-wave observation of GW170817 provided a posterior on H₀ with mode and 68.3% highest posterior density interval of ${H}_{0}={69}_{-8}^{+22}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ (Abbott et al. 2017e, 2019b, 2019c), assuming a flat prior on H₀.

GW190814 is the best localized dark siren, i.e., gravitational-wave source without an electromagnetic counterpart, observed to date, and so it is a good candidate for the statistical cross-correlation method. For a fixed reference cosmology (Ade et al. 2016), the GLADE galaxy catalog (Dálya et al. 2018) is approximately 40% complete at the distance of GW190814 and contains 472 galaxies within the 90% posterior credible volume of GW190814. To obtain a constrain on H₀, we use the methodology described in Abbott et al. (2019c) and the GLADE catalog. We take a flat prior for H₀ ∈ [20, 140] km s⁻¹ Mpc⁻¹ and assign a probability to each galaxy that it is the true host of the event that is proportional to its B-band luminosity. Using the posterior distribution on the distance obtained from the combined PHM samples, we obtain H₀ = ${75}_{-13}^{+59}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ using GW190814 alone (mode and 68.3% highest posterior density interval; the median and 90% symmetric credible interval is ${H}_{0}={83}_{-53}^{+55}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$), which can be compared to ${H}_{0}\,={75}_{-32}^{+40}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ (Soares-Santos et al. 2019) obtained using the dark siren GW170814 alone. The GW190814 result is the most precise measurement from a single dark siren observation to date, albeit comparable to the GW170814 result, which is expected given GW190814's small localization volume (∼39,000 Mpc³). The result is not very constraining, with the 68.3% highest posterior density interval comprising 60% of the prior range. Combining the result for GW190814 with the result obtained from GW170817, we see an improvement over the GW170817-only result, to ${H}_{0}={70}_{-8}^{+17}\,\mathrm{km}\,{{\rm{s}}}^{-1}\,{\mathrm{Mpc}}^{-1}$ (the median and 90% symmetric credible interval is ${H}_{0}={77}_{-23}^{+33}$ km s⁻¹ Mpc⁻¹).This result is not yet sufficiently constraining to provide further insight into current tensions in low and high redshift measurements of the Hubble constant (Verde et al. 2019), but these constraints will continue to improve as further gravitational-wave observations are included (e.g., projections in Chen et al. 2018; Vitale & Chen 2018; Gray et al. 2020; Feeney et al. 2019).