arXiv subjects

Vijay Varma

Publications and source records attributed to Vijay Varma.

At least 19 recordsLinked to original sources

arXiv2026

NRSur7dq4v2: A multi-domain precessing surrogate model with improved accuracy

Numerical relativity simulations provide the most accurate waveforms for binary black hole coalescences, but are prohibitively expensive for direct use in gravitational-wave data analysis. Surrogate models overcome this cost, and NRSur7dq4 is commonly used in parameter estimation for this reason. However, there is evidence in the literature that NRSur7dq4's accuracy could be improved in the merger-ringdown portion of the waveform, which is particularly important for the analysis of high-mass binary black hole events. Motivated by these observations, we construct NRSur7dq4v2, a multi-domain extension of NRSur7dq4 in which overlapping temporal subdomains allow tighter, independent error control over the inspiral and merger-ringdown portions of the waveform before they are smoothly combined. To assess the new model's performance in the merger-ringdown regime, we infer the mass and spin of the remnant black hole from quasi-normal fits to the ringdown portion of the surrogate waveform. We find that NRSur7dq4v2 produces significantly improved remnant mass and spin estimates, with gains of roughly factors of $3$ to $10$ over NRSur7dq4. Compared to NRSur7dq4, NRSur7dq4v2 also includes modes up to $\ell=5$, includes more accurate modeling of certain subdominant modes, and introduces a runtime model-complexity feature that gives users direct control over the tradeoff between evaluation cost and accuracy. Finally, alongside the model development, we have optimized the gwsurrogate package to achieve a fourfold speedup for precessing surrogates. The resulting precessing surrogates called through gwsurrogate are now slightly faster than their LALSimulation counterparts.

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arXiv2026

Fast, accurate, and differentiable: a neural-network surrogate for NRSur7dq4 precessing binary black hole waveforms

We present a neural network surrogate model that emulates the NRSur7dq4 gravitational waveform model for precessing binary black hole mergers. The surrogate decomposes the waveform into constituent quantities and trains an independent multilayer perceptron (MLP) for each. We validate the surrogate against NRSur7dq4 on 10,000 waveforms spanning its full parameter space ($1 \leq q \leq 4$, $|\chi_{A,B}| \leq 0.8$). For representative total masses between 60 and 300 $M_\odot$, median sky-averaged frequency-domain mismatches range from $8.0 \times 10^{-5}$ to $1.7 \times 10^{-4}$, with 95th percentiles below $10^{-3}$. On an NVIDIA L40S GPU the JAX surrogate evaluates a single waveform in about 1 ms end-to-end, roughly 10 times faster than the LALSimulation C implementation of NRSur7dq4, and sustains about 140 times the LALSimulation throughput at batch size 64, making it well suited for both low-latency parameter-estimation samplers and large-scale waveform generation. The full NRSur7dq4 NN waveform-to-likelihood pipeline is implemented in JAX and is differentiable. This is the first neural-network surrogate of a precessing numerical-relativity waveform model to combine validated NR-faithful accuracy with a fully differentiable, GPU-accelerated inference pipeline, enabling gradient-based inference approaches via automatic differentiation including Fisher information matrices, GPU-accelerated nested sampling, gradient-based MCMC and importance sampling.

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arXiv2026

Merger remnant and eccentricity dynamics surrogates for eccentric nonspinning black hole binaries

Accurate models of merger remnants are increasingly important for gravitational-wave science, including precision tests of gravity with ringdown, inference of black-hole populations, and modeling hierarchical mergers. For eccentric binaries, remnant mass, spin, and recoil carry nontrivial imprints of eccentricity that are both physically informative and more challenging to model, yet remain less developed than in the quasi-circular case. We present two new models trained on numerical-relativity (NR) simulations of unequal-mass, non-spinning eccentric binary black holes: NRSurE_q4NoSpin_Remnant, which predicts remnant properties, and NRSurE_q4NoSpin_Dynamics, a time-domain surrogate for the evolution of eccentricity and mean anomaly. Both models are trained on NR simulations over a three-dimensional parameter space with mass ratios $q \leq 4$, eccentricity $e < 0.23$, and mean anomaly $\ell \in [0,2\pi)$ radians, where both $e$ and $\ell$ defined at $t=-1000M$ relative to peak amplitude and $M$ is the total mass. We highlight some applications, including the phenomenological impact of eccentricity on remnant properties and the enhancement or suppression of recoil. We also provide error estimates for all modeled quantities, supporting reliable use in current and future gravitational-wave parameter-estimation analyses. Both models will be made available through open-source codes.

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arXiv2026

Including higher-order modes in a quadrupolar eccentric numerical relativity surrogate using universal eccentric modulation functions

\texttt{gwNRHME} is a framework that converts multi-modal (i.e., containing several spherical harmonic modes) quasi-circular waveforms into their eccentric counterparts, provided the quadrupolar eccentric mode is known, by exploiting universal eccentric modulation functions. Leveraging this framework, we combine the quasi-circular NR surrogate model \texttt{NRHybSur3dq8} with the quadrupolar, non-spinning, eccentric surrogate \texttt{NRSurE\_q4NoSpin\_22} to construct a multi-modal, non-spinning, eccentric model, denoted as \model{}, which includes nine modes: $(2,\{1,2\})$, $(3,\{1,2,3\})$, $(4,\{2,3,4\})$, and $(5,5)$. When compared against 156 eccentric SXS NR waveforms, \model{} achieves median frequency-domain mismatches (computed using the Advanced LIGO design sensitivity) of $\sim 9\times 10^{-5}$, with a standard deviation of $\sim 2 \times 10^{-4}$. To demonstrate the modularity of the framework, we further combine \texttt{NRSurE\_q4NoSpin\_22} with effective-one-body (EOB) models \texttt{SEOBNRv5HM} and \texttt{TEOBResumS-Dali} in their non-spinning limits, yielding eccentric waveforms with median mismatches of $\sim 2\times10^{-4}$ and $\sim 10^{-3}$, respectively, with standard deviation of $\sim 2 \times 10^{-3}$ and $\sim 2 \times 10^{-2}$ respectively. Finally, we provide both a surrogate model, \texttt{gwEccEvolve\_q4NoSpin\_Sur}, and an analytical model, \texttt{gwEccEvNSv2}, for the eccentricity evolution up to $2M$ before merger, based on eccentricity definitions derived from the universal modulation functions. The \texttt{gwNRHME} framework is publicly available through the \texttt{gwModels} package, and the resulting waveform models will be released via the \texttt{gwsurrogate} package.

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arXiv2025

Eccentric binary black holes: A new framework for numerical relativity waveform surrogates

Mounting evidence indicates that some of the gravitational wave signals observed by the LIGO/Virgo/KAGRA observatories might arise from eccentric compact object binaries, increasing the urgency for accurate waveform models for such systems. While for non-eccentric binaries, surrogate models are efficient and accurate, the additional features due to eccentricity have posed a challenge. In this letter, we present a novel method for decomposing eccentric numerical relativity waveforms which makes them amenable to surrogate modelling techniques. We parameterize the inspiral in the radial phase domain, factoring out eccentricity-induced dephasing and thus enhancing compressibility and accuracy. This is combined with a second surrogate for the merger-ringdown in the time-domain and a novel technique to take advantage of the approximate periodicity with radial oscillations during the inspiral. We apply this procedure to the $(2,2)$ mode for non-spinning black hole binaries, and demonstrate that the resulting surrogate, NRSurE_q4NoSpin_22, is able to faithfully reproduce the underlying numerical relativity waveforms, with maximum mismatches of $5\times10^{-4}$ and median mismatches of $2\times10^{-5}$. This technique paves the way for high-accuracy parameter estimation with eccentric models, a key ingredient for astrophysical inference and tests of general relativity.

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arXiv2025

Chase Orbits, not Time: A Scalable Paradigm for Long-Duration Eccentric Gravitational-Wave Surrogates

Orbital eccentricity is a key tracer of the astrophysical origins of compact binaries. Yet it remains absent from routine LIGO-Virgo-KAGRA analyses, in part because of the prohibitive computational cost of generating eccentric template waveforms. The complicated morphology of these waveforms due to the eccentric orbital timescale variations makes it difficult to construct their accurate and efficient surrogate models, especially for waveforms long enough to comprehensively cover the sensitivity bands of current ground-based gravitational-wave detectors. We present a novel and scalable surrogate building technique which makes surrogate modeling of long-duration eccentric binary black hole waveforms both feasible and highly efficient. The technique aims to simplify the harmonic content of intermediate eccentric waveform data-pieces by modeling them in terms of an angular orbital element called the mean anomaly, instead of time. We show that this parameterization yields much more compressed surrogates than the standard time-based parameterizations. We also significantly simplify variations in waveform data-pieces across the parameter space by expressing them in terms of the instantaneous orbital eccentricity and mean anomaly to ease their parametric fitting. Building on these developments, we construct InspiralESIGMASur: a $2.77 \times 10^6M$ (850-1250 orbits) long non-spinning surrogate for the inspiral-only eccentric waveform model InspiralESIGMA [K. Paul et al., Phys. Rev. D 111, 084074 (2025)]. The methods presented in this work make it feasible to build long-duration eccentric surrogates for current as well as future third-generation gravitational-wave detectors.

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arXiv2025

Defining eccentricity for spin-precessing binaries

Standardizing the definition of eccentricity is necessary for unambiguous inference of the orbital eccentricity of compact binaries from gravitational wave observations. In previous works, we proposed a definition of eccentricity for systems without spin-precession that relies solely on the gravitational waveform, is applicable to any waveform model, and has the correct Newtonian limit. In this work, we extend this definition to spin-precessing systems. This simple yet effective extension relies on first transforming the waveform from the inertial frame to the coprecessing frame, and then adopting an amplitude and a phase with reduced spin-induced effects. Our method includes a robust procedure for filtering out spin-induced modulations, which become non-negligible in the small eccentricity and large spin-precession regime. Finally, we apply our method to a set of Numerical Relativity and Effective One Body waveforms to showcase its robustness for generic eccentric spin-precessing binaries. We make our method public via Python implementation in \texttt{gw\_eccentricity}.

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arXiv2025

Measuring spin precession from massive black hole binaries with gravitational waves: insights from time-domain signal morphology

Robustly measuring binary black hole spins via gravitational waves is key to understanding these systems' astrophysical origins, but remains challenging -- especially for high-mass systems, whose signals are short and dominated by the merger. Nonetheless, events like GW190521 show that strong spin precession can indeed be gleaned. In this work, we track how spin precession imprints on simulated high-mass binary black hole signals cycle-by-cycle using time-domain inference. We investigate a suite of signals, all with the same spins and (near-unity) mass ratio -- yielding identical spin evolution -- but different signal-to-noise ratios (SNRs), total masses, and extrinsic angles, all of which affect the observed waveform morphology. We truncate each signal at various times and infer source parameters using only the data before or after each cutoff. The resultant posterior allows us to identify which time segments of each signal inform its spin precession constraints. We find that at a sufficiently high post-peak SNR ($\sim 20$), spin precession can be constrained by the NRSur7dq4 waveform model when just the post-peak data (i.e., ringdown) are visible. Similarly, at a large enough pre-cutoff SNR ($\sim 10$), spin precession can be constrained using only pre-peak data (i.e., inspiral); this occurs for signals with detector-frame total mass $\lesssim 100 M_{\odot}$ at GW190521's full-signal SNR. Finally, we vary the inclination, polarization, and phase angles, finding that their configuration need not be fine-tuned to measure spin precession, even for very high-mass and short signals with two to three observable cycles. We do not find that the same morphological features consistently drive precession constraints: in some signals, precession inference hinges on the relationship between a loud merger and quiet pre-merger cycle, as was the case for GW190521, but this is not generically true.

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arXiv2025

The SXS Collaboration's third catalog of binary black hole simulations

We present a major update to the Simulating eXtreme Spacetimes (SXS) Collaboration's catalog of binary black hole simulations. Using highly efficient spectral methods implemented in the Spectral Einstein Code (SpEC), we have nearly doubled the total number of binary configurations from 2,018 to 3,756. The catalog now densely covers the parameter space with precessing simulations up to mass ratio $q=8$ and dimensionless spins up to $|\vec{\chi}|\le0.8$ with near-zero eccentricity. The catalog also includes some simulations at higher mass ratios with moderate spin and more than 250 eccentric simulations. We have also deprecated and rerun some simulations from our previous catalog (e.g., simulations run with a much older version of SpEC or that had anomalously high errors in the waveform). The median waveform difference (which is similar to the mismatch) between resolutions over the simulations in the catalog is $4\times10^{-4}$. The simulations have a median of 22 orbits, while the longest simulation has 148 orbits. We have corrected each waveform in the catalog to be in the binary's center-of-mass frame and exhibit gravitational-wave memory. We estimate the total CPU cost of all simulations in the catalog to be 480,000,000 core-hours. We find that using spectral methods for binary black hole simulations is over 1,000 times more efficient than much shorter finite-difference simulations of comparable accuracy. The full catalog is publicly available through the sxs Python package and at https://data.black-holes.org .

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arXiv2025

GWSurrogate: A Python package for gravitational wave surrogate models

Fast and accurate waveform models are fundamentally important to modern gravitational wave astrophysics, enabling the study of merging compact objects like black holes and neutron stars. However, generating high-fidelity gravitational waveforms through numerical relativity simulations is computationally intensive, often requiring days to months of computation time on supercomputers. Surrogate models provide a practical solution to dramatically accelerate waveform evaluations (typically tens of milliseconds per evaluation) while retaining the accuracy of computationally expensive simulations. The GWSurrogate Python package provides easy access to these gravitational wave surrogate models through a user-friendly interface. Currently, the package supports 16 surrogate models, each varying in duration, included physical effects (e.g., nonlinear memory, tidal forces, harmonic modes, eccentricity, mass ratio range, precession effects), and underlying solution methods (e.g., Effective One Body, numerical relativity, black hole perturbation theory). GWSurrogate models follow the waveform model conventions used by the LIGO-Virgo-Kagra collaboration, making the package immediately suitable for both theoretical studies and practical gravitational wave data analysis. By enabling rapid and precise waveform generation, GWSurrogate serves as a production-level tool for diverse applications, including parameter estimation, template bank generation, and tests of general relativity.

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arXiv2025

Modeling the BMS transformation induced by a binary black hole merger

Understanding the characteristics of the remnant black hole formed in a binary black hole merger is crucial for conducting gravitational wave astronomy. Typically, models of remnant black holes provide information about their mass, spin, and kick velocity. However, other information related to the supertranslation symmetries of the BMS group, such as the memory effect, is also important for characterizing the final state of the system. In this work, we build a model of the BMS transformation that maps a binary black hole's inspiral frame to the remnant black hole's canonical rest frame. Training data for this model are created using high-precision numerical relativity simulations of quasi-circular systems with mass ratios $q \le 8$ and spins parallel to the orbital angular momentum with magnitudes $\chi_{1}, \chi_{2} \le 0.8$. We use Gaussian Process Regression to model the BMS transformations over the three-dimensional parameter space $\left(q, \chi_{1}^{z}, \chi_{2}^{z}\right)$. The physics captured by this model is strictly non-perturbative and cannot be obtained from post-Newtonian approximations alone, as it requires knowledge of the strong nonlinear effects that are sourced during the merger. Apart from providing the first model of the supertranslation induced by a binary black hole merger, we also find that the kick velocities predicted using Cauchy-characteristic evolution waveforms are, on average, $\sim5\%$ larger than the ones obtained from extrapolated waveforms. Our work has broad implications for improving gravitational wave models and studying the large-scale impact of memory, such as on the cosmological background. The fits produced in this work are available through the Python package $\texttt{surfinBH}$ under the name $\texttt{NRSur3dq8BMSRemnant}$.

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arXiv2025

Impact of eccentricity and mean anomaly in numerical relativity mergers

Accurate modelling of black hole binaries is critical to achieve the science goals of gravitational-wave detectors. Modelling such configurations relies strongly on calibration to numerical-relativity (NR) simulations. Binaries on quasi-circular orbits have been widely explored in NR, however, coverage of the broader 9-dimensional parameter space, including orbital eccentricity, remains sparse. This article develops a new procedure to control orbital eccentricity of binary black hole simulations that enables choosing initial data parameters with precise control over eccentricity and mean anomaly of the subsequent evolution, as well as the coalescence time. We then calculate several sequences of NR simulations that nearly uniformly cover the 2-dimensional eccentricity--mean anomaly space for equal mass, non-spinning binary black holes. We demonstrate that, for fixed eccentricity, many quantities related to the merger dynamics of binary black holes show an oscillatory dependence on mean anomaly. The amplitude of these oscillations scales nearly linearly with the eccentricity of the system. We find that for the eccentricities explored in this work, the magnitude of deviations in various quantities such as the merger amplitude and peak luminosity can approach $\sim5\%$ of their quasi-circular value. We use our findings to explain eccentric phenomena reported in other studies. We also show that methods for estimating the remnant mass employed in the effective-one-body approach exhibit similar deviations, roughly matching the amplitude of the oscillations we find in NR simulations. This work is an important step towards a complete description of eccentric binary black hole mergers, and demonstrates the importance of considering the entire 2-dimensional parameter subspace related to eccentricity.

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arXiv2025

Optimizing Neural Network Surrogate Models: Application to Black Hole Merger Remnants

Surrogate models of numerical relativity simulations of merging black holes provide the most accurate tools for gravitational-wave data analysis. Neural network-based surrogates promise evaluation speedups, but their accuracy relies on (often obscure) tuning of settings such as the network architecture, hyperparameters, and the size of the training dataset. We propose a systematic optimization strategy that formalizes setting choices and motivates the amount of training data required. We apply this strategy on NRSur7dq4Remnant, an existing surrogate model for the properties of the remnant of generically precessing binary black hole mergers and construct a neural network version, which we label NRSur7dq4Remnant_NN. The systematic optimization strategy results in a new surrogate model with comparable accuracy, and provides insights into the meaning and role of the various network settings and hyperparameters as well as the structure of the physical process. Moreover, NRSur7dq4Remnant_NN results in evaluation speedups of up to $8$ times on a single CPU and a further improvement of $2,000$ times when evaluated in batches on a GPU. To determine the training set size, we propose an iterative enrichment strategy that efficiently samples the parameter space using much smaller training sets than naive sampling. NRSur7dq4Remnant_NN requires $O(10^4)$ training data, so neural network-based surrogates are ideal for speeding-up models that support such large training datasets, but at the moment cannot directly be applied to numerical relativity catalogs that are $O(10^3)$ in size. The optimization strategy is available through the gwbonsai package.

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arXiv2025

Surrogate modeling of gravitational waves microlensed by spherically symmetric potentials

The anticipated observation of the gravitational microlensing of gravitational waves (GWs) promises to shed light on a host of astrophysical and cosmological questions. However, extracting the parameters of the lens from the modulated GWs requires accurate modeling of the lensing amplification factor, accounting for wave-optics effects. Analytic solutions to the lens equation have not been found to date, except for a handful of simplistic lens models. While numerical solutions to this equation have been developed, the time and computational resources required to evaluate the amplification factor numerically make large-scale parameter estimation of the lens (and source) parameters prohibitive. On the other hand, surrogate modeling of GWs has proven to be a powerful tool to accurately, and rapidly, produce GW templates at arbitrary points in parameter space, interpolating from a finite set of available waveforms at discrete parameter values. In this work, we demonstrate that surrogate modeling can also effectively be applied to the evaluation of the time-domain microlensing amplification factor $\widetilde{F}(t)$. We show this by constructing $\widetilde{F}(t)$ for two lens models, viz. point-mass lens, and singular isothermal sphere, which notably includes logarithmic divergence behaviour. We find both surrogates reproduce the original lens models accurately, with mismatches $\lesssim 5 \times 10^{-4}$ across a range of plausible microlensed binary black hole sources observed by the Einstein Telescope. This surrogate is between 5 and $10^3$ times faster than the underlying lensing models, and can be evaluated in about 100 ms. The accuracy and efficiency attained by our surrogate models will enable practical parameter estimation analyses of microlensed GWs.

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arXiv2024

High-Precision Ringdown Surrogate Model for Non-Precessing Binary Black Holes

Highly precise and robust waveform models are required as improvements in detector sensitivity enable us to test general relativity with more precision than ever before. In this work, we introduce a spin-aligned surrogate ringdown model. This ringdown surrogate, NRSur3dq8_RD, is built with numerical waveforms produced using Cauchy-characteristic evolution. In addition, these waveforms are in the superrest frame of the remnant black hole allowing us to do a correct analysis of the ringdown spectrum. The novel prediction of our surrogate model is complex-valued quasinormal mode (QNM) amplitudes, with median relative errors of $10^{-2}-10^{-3}$ over the parameter space. Like previous remnant surrogates, we also predict the remnant black hole's mass and spin. The QNM mode amplitude errors translate into median errors on ringdown waveforms of $10^{-4}$. The high accuracy and QNM mode content provided by our surrogate will enable high-precision ringdown analyses such as tests of general relativity. Our ringdown model is publicly available through the python package surfinBH.

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arXiv2024

Gravitational wave surrogate model for spinning, intermediate mass ratio binaries based on perturbation theory and numerical relativity

We present BHPTNRSur2dq1e3, a reduced order surrogate model of gravitational waves emitted from binary black hole (BBH) systems in the comparable to large mass ratio regime with aligned spin ($\chi_1$) on the heavier mass ($m_1$). We trained this model on waveform data generated from point particle black hole perturbation theory (ppBHPT) with mass ratios varying from $3 \leq q \leq 1000$ and spins from $-0.8 \leq \chi_1 \leq 0.8$. The waveforms are $13,500 \ m_1$ long and include all spin-weighted spherical harmonic modes up to $\ell = 4$ except the $(4,1)$ and $m = 0$ modes. We find that for binaries with $\chi_1 \lesssim -0.5$, retrograde quasi-normal modes are significantly excited, thereby complicating the modeling process. To overcome this issue, we introduce a domain decomposition approach to model the inspiral and merger-ringdown portion of the signal separately. The resulting model can faithfully reproduce ppBHPT waveforms with a median time-domain mismatch error of $8 \times 10^{-5}$. We then calibrate our model with numerical relativity (NR) data in the comparable mass regime $(3 \leq q \leq 10)$. By comparing with spin-aligned BBH NR simulations at $q = 15$, we find that the dominant quadrupolar (subdominant) modes agree to better than $\approx 10^{-3} \ (\approx 10^{-2})$ when using a time-domain mismatch error, where the largest source of calibration error comes from the transition-to-plunge and ringdown approximations of perturbation theory. Mismatch errors are below $\approx 10^{-2}$ for systems with mass ratios between $6 \leq q \leq 15$ and typically get smaller at larger mass ratio. Our two models - both the ppBHPT waveform model and the NR-calibrated ppBHPT model - will be publicly available through gwsurrogate and the Black Hole Perturbation Toolkit packages.

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arXiv2024

Optimizing post-Newtonian parameters and fixing the BMS frame for numerical-relativity waveform hybridizations

Numerical relativity (NR) simulations of binary black holes provide precise waveforms, but are typically too computationally expensive to produce waveforms with enough orbits to cover the whole frequency band of gravitational-wave observatories. Accordingly, it is important to be able to hybridize NR waveforms with analytic, post-Newtonian (PN) waveforms, which are accurate during the early inspiral phase. We show that to build such hybrids, it is crucial to both fix the Bondi-Metzner-Sachs (BMS) frame of the NR waveforms to match that of PN theory, and optimize over the PN parameters. We test such a hybridization procedure including all spin-weighted spherical harmonic modes with $|m|\leq \ell$ for $\ell\leq 8$, using 29 NR waveforms with mass ratios $q\leq 10$ and spin magnitudes $|\chi_1|, |\chi_2|\leq 0.8$. We find that for spin-aligned systems, the PN and NR waveforms agree very well. The difference is limited by the small nonzero orbital eccentricity of the NR waveforms, or equivalently by the lack of eccentric terms in the PN waveforms. To maintain full accuracy of the simulations, the matching window for spin-aligned systems should be at least 5 orbits long and end at least 15 orbits before merger. For precessing systems, the errors are larger than for spin-aligned cases. The errors are likely limited by the absence of precession-related spin-spin PN terms. Using $10^5\,M$ long NR waveforms, we find that there is no optimal choice of the matching window within this time span, because the hybridization result for precessing cases is always better if using earlier or longer matching windows. We provide the mean orbital frequency of the smallest acceptable matching window as a function of the target error between the PN and NR waveforms and the black hole spins.

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arXiv2024

GW190521: tracing imprints of spin-precession on the most massive black hole binary

GW190521 is a remarkable gravitational-wave signal on multiple fronts: its source is the most massive black hole binary identified to date and could have spins misaligned with its orbit, leading to spin-induced precession -- an astrophysically consequential property linked to the binary's origin. However, due to its large mass, GW190521 was only observed during its final 3-4 cycles, making precession constraints puzzling and giving rise to alternative interpretations, such as eccentricity. Motivated by these complications, we trace the observational imprints of precession on GW190521 by dissecting the data with a novel time domain technique, allowing us to explore the morphology and interplay of the few observed cycles. We find that precession inference hinges on a quiet portion of the pre-merger data that is suppressed relative to the merger-ringdown. Neither pre-merger nor post-merger data alone are the sole driver of inference, but rather their combination: in the quasi-circular scenario, precession emerges as a mechanism to accommodate the lack of a stronger pre-merger signal in light of the observed post-merger. In terms of source dynamics, the pre-merger suppression arises from a tilting of the binary with respect to the observer. Establishing such a consistent picture between the source dynamics and the observed data is crucial for characterizing the growing number of massive binary observations and bolstering the robustness of ensuing astrophysical claims.

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