arXiv subjects

Matthew Mould

Publications and source records attributed to Matthew Mould.

At least 19 recordsLinked to original sources

arXiv2026

Joint population and strong-lensing inference for resolved gravitational-wave events probes the black-hole merger rate beyond the peak of star formation

Gravitational waves can be lensed by intervening potentials of various scales. Strong lensing leads to underestimated distances and overestimated masses, biasing astrophysical results if not accounted for. I present a novel analysis of the LIGO-Virgo-KAGRA catalog of binary black-hole mergers, simultaneously inferring (1) whether or not each event is strongly lensed, (2) their magnifications if so, and (3) the underlying merger population, using both parametric and nonparametric population models as well as two models for the lensing optical depth. Posterior lensing probabilities do not exceed 1% for any event, so population constraints are consistent with those assuming nondetection of strong lensing or that lensing never occurs. This includes multiple subpopulations over black-hole mass and a component with high aligned spins. Compared to standard analyses, however, there are reductions of order 10% in uncertainty on the redshift at which the merger rate peaks and an order of magnitude in high-redshift rate upper limits. Though modest, these are the first constraints using only resolved events at redshifts where current ground-based gravitational-wave detectors are usually insensitive, at and beyond the peak of star formation.

astro-ph.HE↗

arXiv2026

End-to-End Population Inference from Gravitational-Wave Strain using Transformers

The population of compact binaries encodes information about their astrophysical origins and the expansion of the universe. Hierarchical Bayesian methods infer these properties by combining single-event posteriors. As catalogs grow, however, this approach becomes computationally expensive and is subject to increasing Monte Carlo uncertainty. We introduce Dingo-Pop, a simulation-based framework that infers population posteriors directly from gravitational-wave strain data. The data for each event are embedded into low-dimensional tokens and combined using a transformer trained on simulated catalogs subject to selection effects. This enables (i) population inference without per-event Monte Carlo sampling noise, (ii) amortization across variable catalog sizes using a single network, and (iii) end-to-end inference in about one second. We train a network for catalog sizes of 25 to 1000 events, and obtain well-calibrated posteriors consistent with traditional methods. By avoiding per-event analyses that can take hours to days, Dingo-Pop enables new classes of large-scale injection studies; as an application, we examine how spectral-siren Hubble constant uncertainties change with catalog size.

gr-qc↗

arXiv2026

Gravitational-wave astronomy requires population-informed parameter estimation

Gravitational-wave events are interpreted in terms of Bayesian posteriors for their source properties inferred under unphysical reference priors. Though these parameter estimates are important intermediate data products for downstream analyses, we demonstrate that they are generically biased and therefore should not be used for astrophysical interpretation directly, as is common. Hierarchical parameter estimation is the solution, as joint analysis of the entire catalog of observations reduces statistical uncertainties and actually informs the correct prior, with population-informed event parameters now appropriate for astrophysical interpretation. As an example, we show how the most extreme measurements from a catalog can be derived and used to identify exceptional events from previous and ongoing observing runs, pointing out they are more informative about the population than any individual event. Using LIGO-Virgo-KAGRA data, we thus demonstrate that population inference is not optional to interpret gravitational-wave observations.

gr-qc↗

arXiv2026

Variance of gravitational-wave populations

We quantify the impact of finite catalog size, or "catalog variance," on current gravitational-wave population analyses. The distribution of merging binary black holes is commonly reconstructed via hierarchical Bayesian inference, with uncertainties reported as credible intervals. Such intervals are conditioned on the specific realization of the observed events and are therefore themselves subject to variability arising from the finite size of the catalog. We estimate this "uncertainty on the uncertainty" using statistical bootstrapping applied to data segments containing both detected events and sensitivity injections. Applying this framework to GWTC-4, we find that the inferred population distributions exhibit substantially broader uncertainties than those obtained in a standard single-catalog analysis. In particular, the $\sim 35\,M_\odot$ peak in the primary-mass distribution is largely absorbed by statistical fluctuations once catalog variance is taken into account. Unlike other studies that rely on simulating catalogs by assuming an underlying population, this work provides the first data-driven assessment of the uncertainty intrinsic to the observed gravitational-wave catalog. Accounting for catalog variance is important for drawing robust astrophysical conclusions from gravitational-wave data, avoiding inferences driven by a particular finite realization rather than genuine population features.

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↗

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 $^{12}\mathrm{C}(\alpha,\gamma)^{16}\mathrm{O}$ 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↗

arXiv2025

Simulation-calibrated Bayesian inference for progenitor properties of the microquasar SS 433

SS$\,$433 is one of the most extreme Galactic X-ray binaries, launching semi-relativistic jets and showing clear signs of super-critical accretion onto what is likely a black hole. Yet the properties of the binary system that produced it remain uncertain. To solve the inverse problem of inferring the progenitor properties of binaries that evolve into SS$\,$433-like systems, we use an iterative, simulation-based calibration framework that combines Bayesian inference with the isolated binary-evolution code COSMIC. Using six measured properties of SS$\,$433 and the dynamic nested sampler $\texttt{dynesty}$, we explore a ten-dimensional space of possible progenitor masses, orbits, mass-transfer histories, and natal-kick velocities. This approach identifies the regions of parameter space most consistent with SS$\,$433 and allows us to iteratively refine the resulting progenitor distributions. We find 90% confidence intervals for the progenitor initial primary mass of (8, 11) M$_\odot$, secondary mass of (32, 40) M$_\odot$, orbital period of (136, 2259) days, eccentricity of (0.26, 0.6), common-envelope efficiency of (0.44, 0.76), accreted fraction during stable mass transfer of (0.22, 0.6), and black-hole natal-kick magnitude of (5, 68) km/s. These results show that direct probabilistic inference of X-ray binary progenitors can yield new constraints on the formation of extreme accretion systems like SS$\,$433, which has important implications for theoretical expectations of the population of SS$\,$433-like systems in the Galaxy and their connection with cosmic ray observations.

astro-ph.HE↗

arXiv2025

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.

gr-qc↗

arXiv2025

Evidence of the pair instability gap from black hole masses

Stellar theory predicts a forbidden range of black-hole masses between ${\sim}50$--$130\,M_\odot$ due to pair-instability supernovae, but evidence for such a gap in the mass distribution from gravitational-wave astronomy has proved elusive. Early hints of a cutoff in black-hole masses at ${\sim} 45\,M_\odot$ disappeared with the subsequent discovery of more massive binary black holes. Here, we report evidence of the pair-instability gap in LIGO--Virgo--KAGRA's fourth gravitational wave transient catalog (GWTC-4), with a lower boundary of $44_{-4}^{+5} M_\odot$ (90\% credibility). While the gap is not present in the distribution of \textit{primary} masses $m_1$ (the bigger of the two black holes in a binary system), it appears unambiguously in the distribution of \textit{secondary} masses $m_2$, where $m_2 \leq m_1$. The location of the gap lines up well with a previously identified transition in the binary black-hole spin distribution; binaries with primary components in the gap tend to spin more rapidly than those below the gap. We interpret these findings as evidence for a subpopulation of hierarchical mergers: binaries where the primary component is the product of a previous black-hole merger and thus populates the gap. Our measurement of the location of the pair-instability gap constrains the $S$-factor for $^{12}\rm{C}(\alpha,\gamma)^{16}\rm{O}$ at 300keV to $260_{-108}^{+190}$ keV barns.

astro-ph.HE↗

arXiv2025

Comparing astrophysical models to gravitational-wave data in the observable space

Comparing population-synthesis models to the results of hierarchical Bayesian inference in gravitational-wave astronomy requires a careful understanding of the domain of validity of the models fitted to data. This comparison is usually done using the inferred astrophysical distribution: from the data that were collected, one deconvolves selection effects to reconstruct the generating population distribution. In this paper, we demonstrate the benefits of instead comparing observable populations directly. In this approach, the domain of validity of the models is trivially respected, such that only the relevant parameter space regions as predicted by the astrophysical models of interest contribute to the comparison. With this in mind, it can be useful to fit the observed population directly, rather than effectively deconvolving the selection effects only to fold them back in when reconstructing the observable population. We clarify that unbiased inference of the observable compact-binary population is indeed possible. Crucially, this approach still requires incorporating selection effects, but in a manner that differs from the standard implementation. We apply our observable-space reconstruction to LIGO-Virgo-KAGRA data from their third observing run and illustrate its potential by comparing the results to the predictions of a fiducial population-synthesis model.

gr-qc↗

arXiv2025

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↗

arXiv2025

Black Hole - Neutron Star and Binary Neutron Star Mergers from Population III and II stars

Population III (Pop.$~$III) stars are expected to be massive and to undergo minimal mass loss due to their lack of metals, making them ideal progenitors of black holes and neutron stars. Here, we investigate the formation and properties of binary neutron star (BNS) and black hole-neutron star (BHNS) mergers originating from Pop.$~$III stars, and compare them to their metal-enriched Population II (Pop.$~$II) counterparts, focusing on their merger rate densities (MRDs), primary masses and delay times. We find that, despite the high merger efficiency of Pop.$~$III BNSs and BHNSs, their low star formation rate results in a MRD at least one order of magnitude lower than that of Pop.$~$II stars. The MRD of Pop.$~$III BNSs peaks at redshift $z\sim15$, attaining a value $\mathcal{R}_{\rm BNS}(z\sim15) \sim 15\,\rm Gpc^{-3}\,yr^{-1}$, while the MRD of Pop.$~$III BHNSs is maximum at $z\sim13$, reaching a value $\mathcal{R}_{\rm BHNS}(z\sim13) \sim 2\,\rm Gpc^{-3}\,yr^{-1}$. Finally, we observe that the black hole masses of Pop.$~$III BHNS mergers have a nearly flat distribution with a peak at $\sim 20\,\rm M_{\odot}$ and extending up to $\sim 50\,\rm M_{\odot}$. Black holes in Pop.$~$II BHNS mergers show instead a peak at $\lesssim 15\,\rm M_{\odot}$. We consider these predictions in light of recent gravitational-wave observations in the local Universe, finding that a Pop.$~$III origin is preferred relative to Pop.$~$II for some events.

astro-ph.GA↗

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.

gr-qc↗

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

Reconstructing parametric gravitational-wave population fits from non-parametric results without refitting the data

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation.

astro-ph.HE↗

arXiv2024

Which is which? Identification of the two compact objects in gravitational-wave binaries

Compact objects observed in gravitational-wave astronomy so far always come in pairs and never individually. Identifying the two components of a binary system is a delicate operation that is often taken for granted. The labeling procedure (i.e., which is object "1" and which is object "2") effectively acts as systematics, or, equivalently an unspecified prior, in gravitational-wave data inference. The common approach is to label the objects solely by their masses, on a sample-by-sample basis. We show that object identification can instead be tackled using the posterior distribution as a whole. We frame the problem in terms of constrained clustering -- a flavor of semi-supervised machine learning -- and find that unfolding the labeling systematics can significantly impact, and arguably improve, our interpretation of the data. In particular, the precision of black-hole spin measurements improves by up to 50%, multimodalities and tails tend to disappear, posteriors become closer to Gaussian distributions, and the identification of the nature of the object (i.e. black hole vs. neutron star) is facilitated. We estimate that about 10% of the LIGO/Virgo posterior samples are affected by this relabeling, i.e. they might have been attributed to the other compact object in the observed binaries.

gr-qc↗

arXiv2024

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↗