arXiv subjects

Jacob Golomb

Publications and source records attributed to Jacob Golomb.

17 recordsLinked to original sources

arXiv2026

Low-mass failed supernovae and the $10\,M_{\odot}$ peak in the merging black hole mass distribution

Gravitational-wave observations reveal that the rate of merging black holes drops by $\sim2$ orders of magnitude from component masses $\sim 10\,M_{\odot}$ to $\sim 15\,M_{\odot}$. The increased compactness of the black hole progenitor cores may contribute to the $\sim 10\,M_{\odot}$ overdensity, but cannot fully explain the rate difference. In this paper, we consider the possibility that the overdensity is reinforced by supernova processes that result in efficient black hole formation from direct collapse in a narrow range around $10\, M_{\odot}$. We extend previous studies by considering a distinct subpopulation of failed-supernovae black holes, possibly separated by a gap in the primary mass distribution from the rest of the population. Using 153 observations from the latest GWTC-4.0 catalog, we confirm a strong peak in the primary mass distribution at $10\,M_{\odot}$, with a peak rate density of $7.36_{-3.11}^{+6.35}$ $M_{\odot}^{-1}\mathrm{yr}^{-1} \mathrm{Gpc}^{-3}$. The rate drops sharply and becomes consistent with zero at the 90 % level for primary mass $m_1\in (12.0, 16.1)\, M_{\odot}$, then rises again to confidently nonzero values above $\sim 16\,M_{\odot}$ before falling at higher masses. Our results reveal structure in the mass distribution in the $10-20\,M_{\odot}$ range, with rate changes of multiple orders of magnitude across a few solar masses, consistent with a distinct population of failed-supernova black holes.

astro-ph.HE↗

arXiv2026

Inspiral tests of general relativity and waveform geometry

The phase evolution of gravitational waves encodes critical information about the orbital dynamics of binary systems. In this work, we test the robustness of parameterized tests against unmodeled deviations from general relativity. We demonstrate that these parameterized tests are flexible and sensitive in detecting generic deviations in the waveform using the Cutler-Vallisneri bias formalism. This universality arises from examining the inherent geometry of the waveform signal and understanding how biases manifest. We show how Bayes factors are governed by the intrinsic geometry of the waveform signal manifold when parameterized tests are used to approximate generic violations of GR. We use the singular value decomposition to propose templates that are orthogonal to parameterized tests, identifying degeneracies and enhancing the detection of potential deviations. More broadly, the geometric framework developed here clarifies -- at a fundamental level -- how subtle waveform effects (including orbital eccentricity, spin precession, waveform systematics, and instrumental glitches) can mimic one another in data, and when they are intrinsically distinguishable.

gr-qc↗

arXiv2025

Inference with finite time series II: the window strikes back

Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-KAGRA collaboration, it is conventional to include a term to account for power loss due to this window in the likelihood function. We show that the inclusion of this factor leads to biased inference. The simplest solution to this, omitting the factor, leads to unbiased posteriors and Bayes factor estimates provided the window does not suppress the signal for signal-to-noise ratios $\lesssim O(100)$, but unreliable estimates of the absolute likelihood. Instead, we propose a multi-stage method that yields consistent estimates for the absolute likelihood in addition to unbiased posterior distributions and Bayes factors for signal-to-noise ratios $\lesssim O(1000)$. Additionally, we demonstrate that the commonly held wisdom that using rectangular windows necessarily leads to biased inference is incorrect.

gr-qc↗

arXiv2025

Significant challenges for astrophysical inference with next-generation gravitational-wave observatories

The next generation of gravitational-wave observatories will achieve unprecedented strain sensitivities with an expanded observing band. They will detect ${\cal O}(10^5)$ binary neutron star (BNS) mergers every year, the loudest of which will be in the band for $\approx 90$ minutes with signal-to-noise ratios $\approx 1500$. Current techniques will not be able to determine the astrophysical parameters of the loudest of next-gen BNS signals. We show that subtleties arising from the rotation of the Earth and the free-spectral range of gravitational-wave interferometers dramatically increases the complexity of next-gen BNS signals compared to the one-minute signals seen by LIGO--Virgo. Various compression methods currently relied upon to speed up the most expensive BNS calculations -- reduced-order quadrature, multi-banding, and relative binning -- will no longer be effective. We carry out reduced-order inference on a simulated next-gen BNS signal taking into account the Earth's rotation and the observatories' free-spectral range. We show that standard data compression techniques become impractical, and the full problem becomes computationally infeasible, when we include data below $\approx 16$Hz -- a part of the observing band that is critical for precise sky localisation. We discuss potential paths towards solving this complex problem.

gr-qc↗

arXiv2024

The interplay of astrophysics and nuclear physics in determining the properties of neutron stars

Neutron star properties depend on both nuclear physics and astrophysical processes, and thus observations of neutron stars offer constraints on both large-scale astrophysics and the behavior of cold, dense matter. In this study, we use astronomical data to jointly infer the universal equation of state of dense matter along with two distinct astrophysical populations: Galactic neutron stars observed electromagnetically and merging neutron stars in binaries observed with gravitational waves. We place constraints on neutron star properties and quantify the extent to which they are attributable to macrophysics or microphysics. We confirm previous results indicating that the Galactic and merging neutron stars have distinct mass distributions. The inferred maximum mass of both Galactic neutron stars, $M_{\rm pop, EM}=2.05^{+0.11}_{-0.06}\,M_{\odot}$ (median and 90\% symmetric credible interval), and merging neutron star binaries, $M_{\rm pop, GW}=1.85^{+0.39}_{-0.16}\,M_{\odot}$, are consistent with the maximum mass of nonrotating neutron stars set by nuclear physics, $M_{\rm TOV} =2.28^{+0.41}_{-0.21}\,M_\odot$. The radius of a $1.4\,M_{\odot}$ neutron star is $12.2^{+0.8}_{-0.9}\,$km, consistent with, though $\sim 20\%$ tighter than, previous results using an identical equation of state model. Even though observed Galactic and merging neutron stars originate from populations with distinct properties, there is currently no evidence that astrophysical processes cannot produce neutron stars up to the maximum value imposed by nuclear physics.

astro-ph.HE↗

arXiv2024

GWPopulation: Hardware agnostic population inference for compact binaries and beyond

Since the first direct detection of gravitational waves by the LIGO--Virgo collaboration in 2015, the size of the gravitational-wave transient catalog has grown to nearly 100 events, with more than as many observed during the ongoing fourth observing run. Extracting astrophysical/cosmological information from these observations is a hierarchical Bayesian inference problem. GWPopulation is designed to provide simple-to-use, robust, and extensible tools for hierarchical inference in gravitational-wave astronomy/cosmology. It has been widely adopted for gravitational-wave astronomy, including producing flagship results for the LIGO-Virgo-KAGRA collaborations. While designed to work with observations of compact binary coalescences, GWPopulation may be available to a wider range of hierarchical Bayesian inference problems.

astro-ph.IM↗

arXiv2024

Projections of the uncertainty on the compact binary population background using popstock

The LIGO-Virgo-KAGRA collaboration has announced the detection of almost 100 binary black holes so far, which have been used in several studies to infer the features of the underlying binary black hole population. From these, it is possible to predict the overall gravitational-wave (GW) fractional energy density contributed by black holes throughout the Universe, and thus estimate the gravitational-wave background (GWB) spectrum emitted in the current GW detector band. These predictions are fundamental in our forecasts for background detection and characterization, with both present and future instruments. The uncertainties in the inferred population strongly impact the predicted energy spectrum, and in this paper we present a new, flexible method to quickly calculate the energy spectrum for varying black hole population features such as the mass spectrum and redshift distribution. We implement this method in an open-access package, popstock, and extensively test its capabilities. Using popstock, we investigate how uncertainties in these distributions impact our detection capabilities and present several caveats for background estimation. In particular, we find that the standard assumption that the background signal follows a 2/3 power-law at low frequencies is both waveform and mass-model dependent, and that the signal power-law is likely shallower than previously modelled, given the current waveform and population knowledge.

astro-ph.CO↗

arXiv2024

Using Equation of State Constraints to Classify Low-Mass Compact Binary Mergers

Compact objects observed via gravitational waves are classified as black holes or neutron stars primarily based on their inferred mass with respect to stellar evolution expectations. However, astrophysical expectations for the lowest mass range, $\lesssim 1.2 \,M_\odot$, are uncertain. If such low-mass compact objects exist, ground-based gravitational wave detectors may observe them in binary mergers. Lacking astrophysical expectations for classifying such observations, we go beyond the mass and explore the role of tidal effects. We evaluate how combined mass and tidal inference can inform whether each binary component is a black hole or a neutron star based on consistency with the supranuclear-density equation of state. Low-mass neutron stars experience a large tidal deformation; its observational identification (or lack thereof) can therefore aid in determining the nature of the binary components. Using simulated data, we find that the presence of a sub-solar mass neutron star (black hole) can be established with odds $\sim 100:1$ when two neutron stars (black holes) merge and emit gravitational waves at signal-to-noise ratio $\sim 20$. For the same systems, the absence of a black hole (neutron star) can be established with odds $\sim 10:1$. For mixed neutron star-black hole binaries, we can establish that the system contains a neutron star with odds $\gtrsim 5:1$. Establishing the presence of a black hole in mixed neutron star-black hole binaries is more challenging, except for the case of a $\lesssim 1\,M_{\odot}$ black hole with a $\gtrsim 1\,M_{\odot}$ neutron star companion. On the other hand, classifying each individual binary component suffers from an inherent labeling ambiguity.

astro-ph.HE↗

arXiv2023

Physical Models for the Astrophysical Population of Black Holes: Application to the Bump in the Mass Distribution of Gravitational Wave Sources

Gravitational wave observations of binary black holes have revealed unexpected structure in the black hole mass distribution. Previous studies of the mass distribution employ physically-motivated phenomenological models and infer the parameters that directly control the features of the mass distribution that are allowed in their model, associating the constraints on those parameters with their physical motivations. In this work, we take an alternative approach in which we introduce a model parameterizing the underlying stellar and core-collapse physics and obtaining the remnant black hole distribution as a derived byproduct. In doing so, we directly constrain the stellar physics necessary to explain the astrophysical distribution of black hole properties under a given model. We apply this approach to modeling the mapping between stellar core mass and remnant black hole mass, including the effects of mass loss due to the pulsational pair instability supernova (PPISN) process, which has been proposed as an explanation for the observed excess of black holes at $\sim 35 M_\odot$. Placing constraints on the nuclear reaction rates necessary to explain the PPISN parameters, we conclude that the peak observed at $\sim 35 M_\odot$ is highly unlikely to be a signature from the PPISN process. This procedure can be applied to modeling any physical process that underlies the astrophysical mass distribution. Allowing the parameters of the core-remnant mass relationship to evolve with redshift permits correlated and physically reasonable changes in the location, shape, and amplitude of features in the mass function. We find that the current data are consistent with no redshift evolution in the core-remnant mass relationship, but ultimately place only weak constraints on the change of these parameters.

astro-ph.HE↗

arXiv2023

Growing Pains: Understanding the Impact of Likelihood Uncertainty on Hierarchical Bayesian Inference for Gravitational-Wave Astronomy

Observations of gravitational waves emitted by merging compact binaries have provided tantalising hints about stellar astrophysics, cosmology, and fundamental physics. However, the physical parameters describing the systems, (mass, spin, distance) used to extract these inferences about the Universe are subject to large uncertainties. The most widely-used method of performing these analyses requires performing many Monte Carlo integrals to marginalise over the uncertainty in the properties of the individual binaries and the survey selection bias. These Monte Carlo integrals are subject to fundamental statistical uncertainties. Previous treatments of this statistical uncertainty has focused on ensuring the precision of the inferred inference is unaffected, however, these works have neglected the question of whether sufficient accuracy can also be achieved. In this work, we provide a practical exploration of the impact of uncertainty in our analyses and provide a suggested framework for verifying that astrophysical inferences made with the gravitational-wave transient catalogue are accurate. Applying our framework to models used by the LIGO-Virgo-KAGRA collaboration and in the wider literature, we find that Monte Carlo uncertainty in estimating the survey selection bias is the limiting factor in our ability to probe narrow population models and this will rapidly grow more problematic as the size of the observed population increases.

astro-ph.IM↗

arXiv2023

Angular power spectrum of gravitational-wave transient sources as a probe of the large-scale structure

We present a new, simulation-based inference method to compute the angular power spectrum of the distribution of foreground gravitational-wave transient events. As a first application of this method, we use the binary black hole mergers observed during the LIGO, Virgo, and KAGRA third observation run to test the spatial distribution of these sources. We find no evidence for anisotropy in their angular distribution. We discuss further applications of this method to investigate other gravitational-wave source populations and their correlations to the cosmological large-scale structure.

astro-ph.CO↗

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↗

arXiv2023

Searching for structure in the binary black hole spin distribution

The spins of black holes in merging binaries can reveal information related to the formation and evolution of these systems through their gravitational wave emission. Combining events to infer the astrophysical distribution of black hole spins allows us to determine the relative contribution from different formation scenarios to the population. Many previous works have modeled spin population distributions using parametric models. While these are valuable approaches when the observed population is small, they make strong assumptions about the shape of the underlying distribution and are highly susceptible to biases due to mismodeling. The results obtained with such parametric models are only valid if the allowed shape of the distribution is well-motivated (i.e. for astrophysical reasons). In this work, we relax these prior assumptions and model the spin distributions using a more data-driven approach, modeling these distributions with flexible cubic spline interpolants in order to allow for capturing structures that the parametric models cannot. We find that adding this flexibility to the model substantially increases the uncertainty in the inferred distributions, but find a general trend for lower support at high spin magnitude and a spin tilt distribution consistent with isotropic orientations. We infer that 62 - 87% of black holes have spin magnitudes less than a = 0.5, and 27- 50% of black holes exhibit negative $χ_{\rm eff}$. Using the inferred $χ_{\rm eff}$ distribution, we place a conservative upper limit of 37% for the contribution of hierarchical mergers to the astrophysical BBH population. Additionally, we find that artifacts from unconverged Monte Carlo integrals in the likelihood can manifest as spurious peaks and structures in inferred distributions, mandating the use of a sufficient number of samples when using Monte Carlo integration for population inference.

astro-ph.HE↗

arXiv2022

The curious case of GW200129: interplay between spin-precession inference and data-quality issues

Measurement of spin-precession in black hole binary mergers observed with gravitational waves is an exciting milestone as it relates to both general relativistic dynamics and astrophysical binary formation scenarios. In this study, we revisit the evidence for spin-precession in GW200129 and localize its origin to data in LIGO Livingston in the 20--50\,Hz frequency range where the signal amplitude is lower than expected from a non-precessing binary given all the other data. These data are subject to known data quality issues as a glitch was subtracted from the detector's strain data. The lack of evidence for spin-precession in LIGO Hanford leads to a noticeable inconsistency between the inferred binary mass ratio and precessing spin in the two LIGO detectors, something not expected from solely different Gaussian noise realizations. We revisit the LIGO Livingston glitch mitigation and show that the difference between a spin-precessing and a non-precessing interpretation for GW200129 is smaller than the statistical and systematic uncertainty of the glitch subtraction, finding that the support for spin-precession depends sensitively on the glitch modeling. We also investigate the signal-to-noise ratio $\sim7$ trigger in the less sensitive Virgo detector. Though not influencing the spin-precession studies, the Virgo trigger is grossly inconsistent with the ones in LIGO Hanford and LIGO Livingston as it points to a much heavier system. We interpret the Virgo data in the context of further data quality issues. While our results do not disprove the presence of spin-precession in GW200129, we argue that any such inference is contingent upon the statistical and systematic uncertainty of the glitch mitigation. Our study highlights the role of data quality investigations when inferring subtle effects such as spin-precession for short signals such as the ones produced by high-mass systems.

gr-qc↗

arXiv2021

Hierarchical Inference of Binary Neutron Star Mass Distribution and Equation of State with Gravitational Waves

Gravitational wave observations of binary neutron star mergers provide valuable information about neutron star structure and the equation of state of dense nuclear matter. Numerous methods have been proposed to analyze the population of observed neutron stars and previous work has demonstrated the necessity of jointly fitting the astrophysical distribution and the equation of state in order to accurately constrain the equation of state. In this work, we introduce a new framework to simultaneously infer the distribution of binary neutron star masses and the nuclear equation of state using Gaussian mixture model density estimates which mitigates some of the limitations previously-used methods suffer from. Using our method, we reproduce previous projections for the expected precision of our joint mass distribution and equation of state inference with tens of observations. We also show that mismodeling the equation of state can bias our inference of the neutron star mass distribution. While we focus on neutron star masses and matter effects, our method is widely applicable to population inference problems.

astro-ph.HE↗

arXiv2021

Electromagnetic Emission from a Binary Black Hole Merger Remnant in Plasma: Field Alignment and Plasma Temperature

Comparable-mass black-hole mergers generically result in moderate to highly spinning holes, whose spacetime curvature will significantly affect nearby matter in observable ways. We investigate how the moderate spin of a post-merger Kerr black hole immersed in a plasma with initially uniform density and uniform magnetic field affects potentially observable accretion rates and energy fluxes. Varying the initial specific internal energy of the plasma over two decades, we find very little change in steady-state mass accretion rate or Poynting luminosity, except at the lowest internal energies, where fluxes do not exhibit steady-state behavior during the simulation timescale. Fixing the internal energy and varying the initial fixed magnetic-field amplitude and orientation, we find that the steady-state Poynting luminosity depends strongly on the initial field angle with respect to the black hole spin axis, while the matter accretion rate is more stable until the field angle exceeds $\sim 45\degree$. The proto-jet formed along the black hole spin-axis conforms to a thin, elongated cylinder near the hole, while aligning with the asymptotic magnetic field at large distances.

astro-ph.HE↗

arXiv2019

${\tt PlanetEvidence}$: Planet or Noise?

The work presented here attempts at answering the question: how do we decide when a given adetection is a planet or just residual noise in exoplanet direct imaging data? To this end we present a method implemented within a Bayesian framework: (1) to unify 'source detection', and, 'source characterization' into one single rigorous mathematical framework; (2) to enable an adequate hypothesis testing given the S/N of the data; (3) to enhance the detectability of planets faint signal in the presence of instrumental and background noise and to optimize the characterization of the planet. As a proof of concept we implemented a routine named ${\tt PlanetEvidence}$ that integrates the nested sampling technique (Multinest) with a post-processing technique, the Karhunen-Loeve Image Processing (KLIP), algorithm. This is a first step to recast such post-processing method into a fully Bayesian perspective. We test our approach on real direct imaging data, specifically using GPI data of $β$ Pictoris b, and, on synthetic data. We find that for the former the method strongly favors the presence of a planet (as expected) and recovers the true parameter posterior distributions. While for the latter case our approach allows us to detect (true) dim sources invisible to the naked eye as real planets, rather than background noise, and set a new lower threshold for detection at the 2$σ$ level approximately. Further it allows us to quantify our confidence that a given detection is a real planet and not just residual noise (for example residual speckles). The next natural step is to extend this approach to construct a Bayesian-based algorithm for blind detection, that is, not requiring an initial guess as to the location of the planet. This is the subject of ongoing work.

astro-ph.EP↗