arXiv subjects

Noah E. Wolfe

Publications and source records attributed to Noah E. Wolfe.

11 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.

gr-qc↗

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.

gr-qc↗

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

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

Gravitational wave signals from primordial black holes orbiting solar-type stars

Primordial black holes (PBHs) with masses between $10^{14}$ and $10^{20}$ kg are candidates to contribute a substantial fraction of the total dark matter abundance. When in orbit around the center of a star, which can possibly be a completely interior orbit, such objects would emit gravitational waves, as predicted by general relativity. In this work, we examine the gravitational wave signals emitted by such objects when they orbit typical stars, such as the Sun. We show that the magnitude of the waves that could eventually be detected on Earth from a possible PBH orbiting the Sun or a neighboring Sun-like star within our galaxy can be significantly stronger than those originating from a PBH orbiting a denser but more distant neutron star (NS). Such signals may be detectable by the LISA gravitational-wave detector. In addition, we estimate the contribution that a large collection of such PBH-star systems would make to the stochastic gravitational-wave background (SGWB) within a range of frequencies to which pulsar timing arrays are sensitive.

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↗

arXiv2023

Too small to fail: characterizing sub-solar mass black hole mergers with gravitational waves

The detection of a sub-solar mass black hole could yield dramatic new insights into the nature of dark matter and early-Universe physics, as such objects lack a traditional astrophysical formation mechanism. Gravitational waves allow for the direct measurement of compact object masses during binary mergers, and we expect the gravitational-wave signal from a low-mass coalescence to remain within the LIGO frequency band for thousands of seconds. However, it is unclear whether one can confidently measure the properties of a sub-solar mass compact object and distinguish between a sub-solar mass black hole or other exotic objects. To this end, we perform Bayesian parameter estimation on simulated gravitational-wave signals from sub-solar mass black hole mergers to explore the measurability of their source properties. We find that the LIGO/Virgo detectors during the O4 observing run would be able to confidently identify sub-solar component masses at the threshold of detectability; these events would also be well-localized on the sky and may reveal some information on their binary spin geometry. Further, next-generation detectors such as Cosmic Explorer and the Einstein Telescope will allow for precision measurement of the properties of sub-solar mass mergers and tighter constraints on their compact-object nature.

astro-ph.HE↗

arXiv2023

Gravitational Wave Eigenfrequencies from Neutrino-Driven Core-Collapse Supernovae

Core-collapse supernovae are predicted to produce gravitational waves (GWs) that may be detectable by Advanced LIGO/Virgo. These GW signals carry information from the heart of these catacylsmic events, where matter reaches nuclear densities. Recent studies have shown that it may be possible to infer properties of the proto-neutron star (PNS) via gravitational waves generated by hydrodynamic perturbations of the PNS. However, we lack a comprehensive understanding of how these relationships may change with the properties of core-collapse supernovae. In this work, we build a self-consistent suite of over 1000 exploding core-collapse supernovae from a grid of progenitor masses and metallicities combined with six different nuclear equations of state. Performing a linear perturbation analysis on each model, we compute the resonant gravitational-wave frequencies of the PNS, and we motivate a time-agnostic method for identifying characteristic frequencies of the dominant gravitational-wave emission. From this, we identify two characteristic frequencies, of the early- and late-time signal, that measure the surface gravity of the cold remnant neutron star, and simultaneously constrain the hot nuclear equation of state. However, we find that the details of the core-collapse supernova model, such as the treatment of gravity or the neutrino transport, and whether it explodes, noticeably change the magnitude and evolution of the PNS eigenfrequencies.

astro-ph.HE↗

arXiv2023

Accelerating Tests of General Relativity with Gravitational-Wave Signals using Hybrid Sampling

The Advanced LIGO/Virgo interferometers have observed $\sim 100$ gravitational-wave transients enabling new questions to be answered about relativity, astrophysics, and cosmology. However, many of our current procedures for computing these constraints will not scale well with the increased size of future transient catalogs. We introduce a novel hybrid sampling method in order to more efficiently perform parameterized tests of general relativity with gravitational-wave signals. Applying our method to the binary black hole merger GW150914 and simulated signals we find that our method is approximately an order of magnitude more efficient than the current method with conservative settings for our hybrid analysis. While we have focused on the specific problem of measuring potential deviations from relativity, our method is of much wider applicability to any problem that can be decomposed into a simple and more complex model(s).

gr-qc↗

arXiv2022

A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae

Understanding how matter behaves at the highest densities and temperatures is a major open problem in both nuclear physics and relativistic astrophysics. This physics is often encapsulated in the so-called high-temperature nuclear equation of state, which influences compact binary mergers, core-collapse supernovae, and many more phenomena. One such case is the type (either black hole or neutron star) and mass of the remnant of the core collapse of a massive star. For each of six candidate equations of state, we use a very large suite of spherically symmetric supernova models to generate a suite of synthetic populations of such remnants. We then compare these synthetic populations to the observed remnant population. We thus provide a novel constraint on the high-temperature nuclear equation of state and describe which EOS candidates are more or less favored by this metric.

astro-ph.HE↗