arXiv subjects

Salvatore Vitale

Publications and source records attributed to Salvatore Vitale.

At least 19 recordsLinked to original sources

arXiv2026

Binary-black hole spin population results may be driven by prior degeneracies

Gravitational waves carry information on the spins of merging binary-black holes. The orientations of their spins relative to their orbits ("tilts")---while challenging to measure---differentiate the astrophysical formation mechanisms by which merging black hole binaries may form. Multiple analyses have reported tentative evidence that black hole spins are preferentially oriented in the plane of the binary orbit. We show that these results are likely extrapolated from more easily measurable "effective spin" parameters which characterize the gravitational-wave inspiral of black hole binaries but may not uniquely constrain their astrophysical formation. In particular, we reproduce a peak in the spin tilt distribution with information about the marginal effective spin distributions alone. We propose a geometric picture to compare constraints on effective spins to standard population modeling assumptions, suggesting that current inferences of a preferred spin orientation are spurious.

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arXiv2026

Reducing systematic uncertainties in gravitational-wave population analyses by increasing the detection threshold

From catalogs of gravitational-wave transients, the population-level properties of their sources and the formation channels of merging compact binaries can be constrained. However, astrophysical conclusions can be biased by misspecification or misestimation of the population likelihood. Despite detection thresholds on the false-alarm rate (FAR) or signal-to-noise ratio (SNR), the current catalog is likely contaminated by noise transients. Further, computing the population likelihood becomes less accurate as the catalog grows. Current methods to address these challenges often scale poorly with the number of events and potentially become infeasible for future catalogs. Here, we evaluate a simple remedy: increasing the significance threshold for including events in population analyses. To determine the efficacy of this approach, we analyze simulated catalogs of up to 1600 gravitational-wave signals from black-hole mergers using full Bayesian parameter estimation with current detector sensitivities. We show that the growth in statistical uncertainty about the black-hole population, as we analyze fewer events but with higher SNR, depends on the source parameters of interest. When the SNR threshold is raised from 11 to 15---reducing our catalog size by two--thirds---we find that statistical uncertainties on the mass distribution only grow by a few 10% and constraints on the spin distribution are essentially unchanged; meanwhile, uncertainties on the high-redshift cosmic merger rate more than double. Simultaneously, numerical uncertainty in the estimate of the population likelihood more than halves, allowing us to ensure unbiased inference without additional computational expense. Our results demonstrate that focusing on higher-significance events is an effective way to facilitate robust astrophysical inference with growing gravitational-wave catalogs.

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arXiv2026

No model-independent evidence for a peak in binary black hole spin (mis)alignments

The degree of black-hole spin-orbit misalignment ("tilts") in the astrophysical population could be a powerful diagnostic to distinguish between binary formation in isolation, in dynamical environments, or in hierarchical triples. However, robust population-level spin tilt measurements are complicated by model misspecification as well as numerical and Poisson variance, ultimately owing to poor single-event constraints on tilts. Motivated by reports of a possible peak in the spin tilt distribution, we analyze the fourth LIGO-Virgo-KAGRA gravitational-wave transient catalog to test for preferred spin orientations at different black hole masses. We find that a peak in spin tilts is not statistically significant nor model independent. Since the data cannot be used to reliably identify subpopulations based on their spin tilt properties, we also consider a complementary approach: measuring the spin magnitude and tilt distributions at fixed mass scales. We find no confident correlation between mass and spin tilt, but we do confirm a confident correlation between spin magnitude and mass, corroborating recent analyses.

astro-ph.HE↗

arXiv2026

Nowhere left to hide: revealing realistic gravitational-wave populations in high dimensions and high resolution with PixelPop

The origins of merging compact binaries observed by the LIGO-Virgo-KAGRA gravitational-wave detectors remain uncertain, with multiple astrophysical channels possibly contributing to the merger rate. Formation processes can imprint nontrivial correlations in the underlying distribution of source properties, but current understanding of the overall population relies heavily on simplified and uncorrelated parametric models. In this work, we use PixelPop-a high-resolution Bayesian nonparametric model with minimal assumptions-to analyze multidimensional correlations in the astrophysical distribution of masses, spins, and redshifts of black-hole mergers from mock gravitational-wave catalogs constructed using population-synthesis simulations. With full parameter estimation on 400 detections at current sensitivities, we show explicitly that neglecting population-level correlations biases inference. In contrast, modeling all significant correlations with PixelPop allows us to correctly measure the astrophysical merger rate across all source parameters. We then propose a nonparametric method to distinguish between different formation channels by comparing the PixelPop results back to astrophysical simulations. For our simulated catalog, we find that only formation channels with significantly different physical processes are distinguishable, whereas channels that share evolutionary stages are not. Given the substantial uncertainties in source formation, our results highlight the necessity of multidimensional astrophysics-agnostic models like PixelPop for robust interpretation of gravitational-wave catalogs.

astro-ph.HE↗

arXiv2026

Signatures of a subpopulation of hierarchical mergers in the GWTC-4 gravitational-wave dataset

Repeated black-hole mergers in dense stellar clusters are a plausible mechanism to populate the predicted gap in black hole masses due to the pair-instability supernova process. These hierarchical mergers carry distinct spin characteristics relative to first-generation black holes. We introduce an astrophysically motivated model in the joint space of effective inspiral and precessing spins, which captures the dominant spin dynamics expected for hierarchical mergers. We find decisive evidence for a transition at $m_1 = 46.2_{-7.2}^{+12.6} M_\odot$, above which the population is nearly entirely hierarchical, a location consistent with the anticipated onset of the pair-instability gap. We also infer a global peak in the hierarchical merger rate at $m_1 = 15.7_{-1.1}^{+3.2} M_\odot$. The existence of low- and high-mass subpopulations of higher-generation black holes suggests the contribution of both near-solar-metallicity and metal-poor star clusters to the hierarchical merger population. Our results reinforce the growing evidence for detailed, mass-dependent substructure in the spin distribution of the binary black hole population.

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arXiv2026

Evidence for additional structure in the effective spin distribution hints at multiple formation pathways in GWTC-5.0

The distribution of the effective inspiral spin ($χ_\mathrm{eff}$) of the binary black holes detected by LIGO-Virgo-KAGRA can shed light on their formation pathways. We analyze the GWTC-5.0 dataset with two models-one flexible, one fully parametric-that jointly describe $χ_\mathrm{eff}$ and primary mass. We clarify that the previously-reported skewness in the $χ_\mathrm{eff}$ distribution is better understood as additional structure beyond a non-skewed Gaussian bulk centered at small $χ_\mathrm{eff}$. This additional structure extends to larger $|χ_\mathrm{eff}|$, a result previously reported using GWTC-4.0 data. We measure the asymmetry of the distribution of $χ_\mathrm{eff}$ outside the Gaussian bulk from the data. With both the parametric and the flexible analyses, we find suggestive evidence for a mass-dependent excess of positive $χ_\mathrm{eff}$ over negative ones outside the Gaussian bulk. We find only a mild statistical preference for a negative $χ_\mathrm{eff}$ component outside the Gaussian bulk. This preference is highest at $m_1 \in [47,65]\,M_\odot$, with $13\text{:}1$ odds. Excluding the event GW191109, whose data have known data-quality issues, reduces these odds to $5\text{:}1$. If $χ_\mathrm{eff}$ outside the Gaussian bulk are produced by hierarchical mergers-as has been suggested-then a fraction of those mergers may be produced in environments that can generate a surplus of binaries with positive $χ_\mathrm{eff}$, such as the disks of active galactic nuclei.

astro-ph.HE↗

arXiv2026

Measurement prospects for the pair-instability mass cutoff with gravitational waves

Pair-instability supernovae leave behind no compact remnants, resulting in a predicted gap in the distribution of stellar black-hole masses. Gravitational waves from binary black-hole mergers probe the relevant mass range and analyses of the LIGO-Virgo-KAGRA catalog (GWTC-4) indicate a possible mass cutoff at $40$-$50\,M_\odot$. However, the robustness of this result remains unclear. To this end, using full Bayesian parameter estimation, we simulate gravitational-wave catalogs with and without such a mass cutoff, then test whether its presence or absence is correctly inferred with parametric population models. For catalogs similar to GWTC-4, confident identification of a cutoff is not guaranteed, but the best constraints among our simulations are compatible with results from GWTC-4 when the model includes a cutoff. Conversely, spurious identification of a cutoff is unlikely. For catalogs expected by the end of the O4 observing run, uncertainty in the cutoff mass is reduced by $\gtrsim20\,\%$, but a cutoff at 40-50$M_\odot$ yields only a lower bound on the $^{12}\mathrm{C}(α,γ)^{16}\mathrm{O}$ reaction rate, our most stringent constraints on the S-factor at $300\,\mathrm{keV}$ being $S_{300}\gtrsim125\,\mathrm{keV}\,\mathrm{b}$ at $90\,\%$ credibility. Relative uncertainties on the Hubble parameter $H_0$ from gravitational-wave data alone can still be up to $100\,\%$. We also analyze GWTC-4 with the nonparametric PixelPop population model, finding that some mass features are more prominent than in parametric models but a sharp cutoff is not required. However, the parametric model passes a likelihood-based predictive test in GWTC-4 and the PixelPop results are consistent with those from our simulated catalogs with a cutoff. Such tests are necessary to make astrophysical claims from gravitational-wave catalogs.

astro-ph.HE↗

arXiv2026

Neural Bayesian updates to populations with growing gravitational-wave catalogs

As gravitational-wave catalogs grow, they will become increasingly computationally expensive to analyze in their entirety, especially when inferring astrophysical source populations with high-dimensional, flexible models. Bayesian statistics offers a natural remedy, letting us update our knowledge of physical models as new data arrive, without re-analyzing existing data. However, doing so requires the posterior probability density of model parameters for previous observations, which is typically intractable. Here, we use variational neural posterior estimation to rapidly update the inferred population of binary black holes as data are observed in gravitational-wave detectors. We apply this approach to real and simulated catalogs analyzed with both low- and high-dimensional population models, testing the reliability of three update cadences: with new catalogs of sources, month by month during an observing run, and as each new signal arrives. We investigate the success and failure modes of neural sequential updates, finding that the robustness of updating is sensitive to the information contained in each update and that updating is most effective when performed with larger segments of data. We outline one additional scientific application enabled by Bayesian updating: identification of events that are individually informative about the population. Neural Bayesian updates to astrophysical population models also provide efficient likelihood representations for joint analyses with other data, e.g., standard-siren cosmology, and similar methods can be used to perform Bayesian stochastic background searches.

astro-ph.IM↗

arXiv2025

When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference

The coming years of gravitational wave astrophysics promises thousands of new detections, which can unlock fundamental scientific insights if the information in each observation can be properly synthesized into a coherent picture. State-of-the-art approaches often accomplish this with hierarchical Bayesian inference. However, this typically relies on Monte Carlo approximations that are already very expensive in current data, and may become prohibitively so in the future. In this paper we show how this process can be understood from a first-principles statistical approach. We derive an error estimator $\hat{E}$ for quantifying the amount of information that is lost due to the Monte Carlo approximation and recommend that this error is limited to no more than $\hat{E} \lesssim 0.2$ bits for reliable inference. We also show that the hierarchical likelihood estimator is biased but may be corrected. Finally, we show some practical examples for inference on synthetic gravitational-wave population inference, demonstrating that simple models with strong assumptions can be much more stable to Monte Carlo uncertainty than those with weaker assumptions. We also provide a \texttt{pip}-installable package \texttt{population-error} with which analysts can calculate the error statistics $\hat{E}$.

astro-ph.HE↗

arXiv2025

PixelPop: High Resolution Nonparameteric Inference of Gravitational-Wave Populations in Multiple Dimensions

The origins of merging compact binaries observed by gravitational-wave detectors remains highly uncertain. Several astrophysical channels may contribute to the overall merger rate, with distinct formation processes imprinted on the structure and correlations in the underlying distributions of binary source parameters. In the absence of confident theoretical models, the current understanding of this population mostly relies on simple parametric models that make strong assumptions and are prone to misspecification. Recent work has made progress using more flexible nonparametric models, but detailed measurement of the multidimensional population remains challenging. In pursuit of this, we present PixelPop-a high resolution Bayesian nonparametric model to infer joint distributions and parameter correlations with minimal assumptions. PixelPop densely bins the joint parameter space and directly infers the merger rate in each bin, assuming only that bins are coupled to their nearest neighbors. We demonstrate this method on mock populations with and without bivariate source correlations, employing several statistical metrics for information gain and correlation significance to quantify our nonparametric results. We show that PixelPop correctly recovers the true populations within posterior uncertainties and offers a conservative assessment of population-level features and parameter correlations. Its flexibility and tractability make it a useful data-driven tool to probe gravitational-wave populations in multiple dimensions.

astro-ph.HE↗

arXiv2025

Nonparametric analysis of correlations in the binary black hole population with LIGO-Virgo-KAGRA data

Formation channels of merging compact binaries imprint themselves on the distributions and correlations of their source parameters, but current understanding of this population is hindered by simplified parametric models. We overcome such limitations using PixelPop [Heinzel et al. (2025)]-our Bayesian nonparametric multidimensional population model. We analyze data from the first three LIGO-Virgo-KAGRA observing runs and make high resolution, minimally modeled measurements of the pairwise distributions of binary black hole masses, redshifts, and spins. We find no evidence that the mass spectrum evolves over redshift and show that such measurements are fundamentally limited by the detector horizon. We find support for correlations of the spin distribution with binary mass ratio and redshift, but at reduced significance compared to overly constraining parametric models. Confident data-driven conclusions about population-level correlations with flexible models like PixelPop will require more informative gravitational-wave catalogs.

astro-ph.HE↗

arXiv2025

Rapid inference and comparison of gravitational-wave population models with neural variational posteriors

The LIGO-Virgo-KAGRA catalog has been analyzed with an abundance of different population models due to theoretical uncertainty in the formation of gravitational-wave sources. To expedite model exploration, we introduce an efficient and accurate variational Bayesian approach that learns the population posterior with a normalizing flow and serves as a drop-in replacement for existing samplers. With hardware acceleration, inference takes just seconds for the current set of black-hole mergers and readily scales to larger catalogs. The trained posteriors provide an arbitrary number of independent samples with exact probability densities, unlike established stochastic sampling algorithms, while requiring up to three orders of magnitude fewer likelihood evaluations and as few as $\mathcal{O}(10^3)$. Provided the posterior support is covered, discrepancies can be addressed with smoothed importance sampling, which quantifies a goodness-of-fit metric for the variational approximation while also estimating the evidence for Bayesian model selection. Neural variational inference thus enables interactive development, analysis, and comparison of population models, making it a useful tool for astrophysical interpretation of current and future gravitational-wave observations.

astro-ph.IM↗

arXiv2025

The Long Road to Alignment: Measuring Black Hole Spin Orientation with Expanding Gravitational-Wave Datasets

Measuring the distribution of spin tilts-the angles between the spin vectors and the binary orbital angular momentum-in stellar-mass binary black holes detected by LIGO-Virgo-KAGRA would provide valuable insight into their astrophysical origins. Analyses of the 69 binary black holes detected through LIGO-Virgo-KAGRA's third observing run yielded model-dependent conclusions, particularly regarding whether the spin tilt distribution exhibits a peak near alignment, as expected for binaries formed in galactic fields. In this work, we simulate populations of up to 1500 binary black hole systems with parameters consistent with the default GWTC-3 analysis, while introducing a correlation that favors small spin tilts for binaries with mass ratios near unity. We find that: (a) spurious peaks away from perfect alignment are possible even with catalogs of up to 300 sources; (b) establishing a definitive peak at alignment remains difficult even with 1500 detections; (c) integrated measurements -- such as the fraction of events with tilt angles smaller than $10^\circ$ or greater than $90^\circ$ -- are more robust and should be preferred, achieving relative $90\%$ credible uncertainties of $\sim20\%-80\%$ with 1500 sources; and (d) even with the largest simulated catalogs, evidence for a mass ratio-tilt correlation remains inconclusive. Our results suggest that identifying the formation channels of merging black holes using spin tilts will remain challenging, but that model-independent measurements may yield more informative insights over model parameters themselves.

astro-ph.HE↗

arXiv2025

Constraining Population III stellar demographics with next-generation gravitational-wave observatories

Next-generation gravitational-wave observatories will reach farther into the universe than currently possible, revealing black-hole mergers from early stellar binary systems such as Population III stars, whose properties are currently poorly constrained. We develop a method to infer the properties of their progenitor populations from gravitational-wave catalogs. Using Bayesian deep learning, we train an emulator for population-synthesis predictions of black-hole merger properties across redshift as a function of the initial stellar mass function, crucially accounting for systematic uncertainty due to the finite number of training simulations. Combined with a nonparametric model for star formation history, we analyze catalogs containing both Population I/II and III sources simulated with full Bayesian parameter estimation for a detector network of Cosmic Explorer and Einstein Telescope with one year of observing time. We demonstrate our ability to separate these two populations at high redshifts where both make comparable contributions to the black-hole merger rate, excluding a Population III merger rate of zero at nearly 100% credibility. Moreover, we can place meaningful constraints on the Population III progenitor distributions; in particular, we constrain the spectral index of the initial mass function to within roughly +/-0.5 of the true value and the log of the star formation rate density to within ~25% over redshifts 10 to 20. By leveraging astrophysics-informed and astrophysics-agnostic models, we demonstrate the discriminative power of our combined inference approach and highlight the potential of next-generation gravitational-wave observatories to uncover the details of high-redshift stellar populations.

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arXiv2025

Impact of calibration uncertainties on Hubble constant measurements from gravitational-wave sources

Gravitational-wave (GW) detections of electromagnetically bright compact binary coalescences can provide an independent measurement of the Hubble constant $H_0$. In order to obtain a measurement that could help arbitrate the existing tension on $H_0$, one needs to fully understand any source of systematic biases for this approach. In this study, we aim to understand the impact of instrumental calibration errors (CEs) on the measurements of the luminosity distance, $D_L$, and the inferred $H_0$ value. We simulate binary neutron star mergers (BNSs), as detected by a network of Advanced LIGO and Advanced Virgo interferometers at their design sensitivity. We artificially add CEs equal to exceptionally large values experienced in LIGO-Virgo's third observing run (O3). We find that for individual BNSs at a network signal-to-noise ratio of 50, the systematic errors on $D_L$ - and hence $H_0$ - are still smaller than the statistical uncertainties. The biases become more significant when we combine multiple events to obtain a joint posterior on $H_0$. In the rather unrealistic case that the data around each detection is affected by the same CEs corresponding to the worst offender of O3, the true $H_0$ value would be excluded from the 90% credible interval after we combine $\sim$40 sources. If instead, 10% of the sources suffer from severe CEs, the true value of $H_0$ is included in the 90% credible interval even after 100 sources.

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arXiv2024

Multi-messenger Astrophysics of Black Holes and Neutron Stars as Probed by Ground-based Gravitational Wave Detectors: From Present to Future

The ground-based gravitational wave (GW) detectors LIGO and Virgo have enabled the birth of multi-messenger GW astronomy via the detection of GWs from merging stellar-mass black holes (BHs) and neutron stars (NSs). GW170817, the first binary NS merger detected in GWs and all bands of the electromagnetic spectrum, is an outstanding example of the impact that GW discoveries can have on multi-messenger astronomy. Yet, GW170817 is only one of the many and varied multi-messenger sources that can be unveiled using ground-based GW detectors. In this contribution, we summarize key open questions in the astrophysics of stellar-mass BHs and NSs that can be answered using current and future-generation ground-based GW detectors, and highlight the potential for new multi-messenger discoveries ahead.

astro-ph.HE↗

arXiv2024

Characterizing Gravitational Wave Detector Networks: From A$^\sharp$ to Cosmic Explorer

Gravitational-wave observations by the Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A#, given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped Cosmic Explorer observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein Telescope with 10 km arms in Europe. The presence of one or two A# observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy and precision measurement of the Hubble parameter. Two Cosmic Explorer observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming multimessenger astronomy. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two Cosmic Explorer observatories is critical for accomplishing all the identified science metrics. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A# observatories.

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arXiv2024

The impact of selection biases on tests of general relativity with gravitational-wave inspirals

Tests of general relativity with gravitational wave observations from merging compact binaries continue to confirm Einstein's theory of gravity with increasing precision. However, these tests have so far only been applied to signals that were first confidently detected by matched-filter searches assuming general relativity templates. This raises the question of selection biases: what is the largest deviation from general relativity that current searches can detect, and are current constraints on such deviations necessarily narrow because they are based on signals that were detected by templated searches in the first place? In this paper, we estimate the impact of selection effects for tests of the inspiral phase evolution of compact binary signals with a simplified version of the GstLAL search pipeline. We find that selection biases affect the search for very large values of the deviation parameters, much larger than the constraints implied by the detected signals. Therefore, combined population constraints from confidently detected events are mostly unaffected by selection biases, with the largest effect being a broadening at the $\sim10$ % level for the $-1$PN term. These findings suggest that current population constraints on the inspiral phase are robust without factoring in selection biases. Our study does not rule out a disjoint, undetectable binary population with large deviations from general relativity, or stronger selection effects in other tests or search procedures.

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