arXiv subjects

Reed Essick

Publications and source records attributed to Reed Essick.

At least 19 recordsLinked to original sources

arXiv2026

Beyond what's observed: hierarchical inference of Gaia astrometric compact-object binaries

Astrometry from the Gaia mission has revealed a large and homogeneous population of binaries containing compact objects, including more than 3000 white dwarf--main sequence (WDMS) binaries with orbital periods of $P_{\rm orb}\sim100$--$1000$ d. Most of these binaries likely underwent mass transfer from asymptotic giant branch donors, but their evolutionary histories remain uncertain. We develop a hierarchical Bayesian inference model to constrain the intrinsic properties of the Gaia WDMS binary population while accounting for measurement uncertainties and the survey selection function. We infer the distributions of orbital period, component masses, and eccentricity, as well as the total space density. We find that the intrinsic orbital-period distribution follows a declining power law, $p(P_{\rm orb})\propto P_{\rm orb}^{-0.445\pm0.048}$, and the WD mass distribution is sharply peaked near $0.6\,M_\odot$, with only ~1% of systems hosting WDs more massive than $0.8\,M_\odot$. The inferred MS-star mass distribution is relatively flat down to ~$0.3\,M_{\odot}$, which is unexpected if the population is dominated by products of stable mass transfer with a fixed critical mass ratio. The eccentricity distribution peaks at a nonzero value, $e=0.046\pm0.001$, suggesting incomplete circularization or eccentricity excitation. We infer a midplane space density of ~$3\times10^4\,{\rm kpc}^{-3}$ for WDMS binaries in the parameter space probed by Gaia DR3. These properties provide empirical benchmarks for future work to constrain binary evolution models. We also apply the framework to Gaia black hole and neutron star binaries, inferring that $30^{+66}_{-20}$ and $614^{+203}_{-149}$ such systems exist within 2 kpc, respectively. This framework will enable detailed population-level constraints on a wide range of systems identified from the much larger catalog of astrometric orbits expected from Gaia DR4.

astro-ph.SR↗

arXiv2026

What You Don't Know Won't Hurt You: Self-Consistent Hierarchical Inference with Unknown Follow-up Selection Strategies

Many astronomical surveys prompt follow-up observations, but the decision process through which candidates are selected for follow-up can be difficult to model. This poses a challenge when inferring properties of the intrinsic population of astrophysical sources, rather than those of the set of objects detected by the survey and often-incomplete follow-up observations. We alleviate this problem by demonstrating that explicitly modeling of the follow-up selection process is not required for self-consistent inference of the intrinsic population. Using the framework of hierarchical Bayesian inference, we show that the intrinsic population can be accurately inferred even when the decision to follow up candidates strongly correlates with latent parameters of interest. We provide several worked examples, showing that the precision of posterior constraints can depend on the follow-up process and that one may have to model a population of contaminants if the initial selection is imperfect. Our result could dramatically simplify population inference that incorporates uncoordinated follow-up from multiple observers triggered by the deluge of candidates from surveys like LSST, Gaia, and next-generation gravitational-wave interferometers.

astro-ph.IM↗

arXiv2026

Semianalytic Sensitivity Estimates for Out-of-Bank Gravitational-Wave Signals

Estimating the sensitivity of gravitational-wave searches is important for a wide variety of scientific applications spanning astrophysics and fundamental physics. In this work, we develop a fast semianalytic approximation for estimating matched-filter search sensitivity to physical effects not explicitly modeled in the template bank. This approximation utilizes fitting factors, i.e., the maximum overlap of a candidate signal over the search bank. As illustrations, we compare our estimates to the actual performance of searches against spinning binary neutron stars, and evaluate search sensitivity to compact binaries possessing orbital eccentricity or deviations from general relativity. Our work thus paves the way for fast sensitivity estimates for a variety of applications, including unmodeled effects in template banks such as deviations from general relativity, environmental effects, gravitational lensing, and waveform/calibration systematics.

astro-ph.IM↗

arXiv2026

Guesswork in the gap: the impact of uncertainty in the compact binary population on source classification

The nature of the compact objects within the supposed "lower mass gap" remains uncertain. Observations of GW190814 and GW230529 highlight the challenges gravitational waves face in distinguishing neutron stars from black holes. Interpreting these systems is especially difficult because classifications depend simultaneously on measurement noise, compact binary population models, and equation of state (EOS) constraints on the maximum neutron star mass. We analyze 66 confident events from GWTC-3 to quantify how the probability of a component being a neutron star, P(NS), varies across the population. The effects are substantial, the dominant drivers of classification are the pairing preferences of neutron stars with other compact objects, and the neutron star spin distributions. The data reveals that P(NS) varies between 1% - 67% for GW230529's primary and between 51% - 100% for GW190425's primary. By contrast, P(NS) for GW190814's secondary varies by <10%, demonstrating robustness from its high signal-to-noise ratio and small mass ratio. Analysis using EOS information tends to affect P(NS) through the inferred maximum neutron star mass rather than the maximum spin. As it stands, P(NS) remains sensitive to numerous population parameters, limiting its reliability and potentially leading to ambiguous classifications of future GW events.

astro-ph.HE↗

arXiv2025

Unified nonparametric equation-of-state inference from the neutron-star crust to perturbative-QCD densities

Perturbative quantum chromodynamics (pQCD), while valid only at densities exceeding those found in the cores of neutron stars, could provide constraints on the dense-matter equation of state (EOS). In this work, we examine the impact of pQCD information on the inference of the EOS using a nonparametric framework based on Gaussian processes (GPs). We examine the application of pQCD constraints through a "pQCD likelihood," and verify the findings of previous works; namely, a softening of the EOS at the central densities of the most massive neutron stars and a reduction in the maximum neutron-star mass. Although the pQCD likelihood can be easily integrated into existing EOS inference frameworks, this approach requires an arbitrary selection of the density at which the constraints are applied. The EOS behavior is also treated differently on either side of the chosen density. To mitigate these issues, we extend the EOS model to higher densities, thereby constructing a "unified" description of the EOS from the neutron-star crust to densities relevant for pQCD. In this approach the pQCD constraints effectively become part of the prior. Since the EOS is unconstrained by any calculation or data between the densities applicable to neutron stars and pQCD, we argue for maximum modeling flexibility in that regime. We compare the unified EOS with the traditional pQCD likelihood, and although we confirm the EOS softening, we do not see a reduction in the maximum neutron-star mass or any impact on macroscopic observables. Though residual model dependence cannot be ruled out, we find that pQCD suggests the speed of sound in the densest neutron-star cores has already started decreasing toward the asymptotic limit; we find that the speed of sound squared at the center of the most massive neutron star has an upper bound of $\sim 0.5$ at the $90\%$ level.

nucl-th↗

arXiv2025

Compact Binary Coalescence Sensitivity Estimates with Injection Campaigns during the LIGO-Virgo-KAGRA Collaborations' Fourth Observing Run

We describe the effort to characterize gravitational-wave searches and detector sensitivity to different types of compact binary coalescences during the LIGO-Virgo-KAGRA Collaborations' fourth observing run. We discuss the design requirements and example use cases for this data product, constructed from $> 4.33\times10^8$ injections during O4a alone. We also identify subtle effects with high confidence, like diurnal duty cycles within detectors. This paper accompanies a public data release of the curated injection set, and the appendixes give detailed examples of how to use the publicly available data.

gr-qc↗

arXiv2025

Nonparametric extensions of nuclear equations of state: probing the breakdown scale of relativistic mean-field theory

Phenomenological calculations of the properties of dense matter, such as relativistic mean-field theories, represent a pathway to predicting the microscopic and macroscopic properties of neutron stars. However, such theories do not generically have well-controlled uncertainties and may break down within neutron stars. To faithfully represent the uncertainty in this breakdown scale, we develop a hybrid representation of the dense-matter equation of state, which assumes the form of a relativistic mean-field theory at low densities, while remaining agnostic to any nuclear theory at high densities. To achieve this, we use a nonparametric equation of state model to incorporate the correlations of the underlying relativistic mean-field theory equation of state at low pressures and transition to more flexible correlations above some chosen pressure scale. We perform astrophysical inference under various choices of the transition pressure between the theory-informed and theory-agnostic models. We further study whether the chosen relativistic mean-field theory breaks down above some particular pressure and find no such evidence. Using simulated data for future astrophysical observations at about two-to-three times the precision of current constraints, we show that our method can identify the breakdown pressure associated with a potential strong phase transition.

nucl-th↗

arXiv2025

Distinguishing between Black Holes and Neutron Stars within a Population of Weak Tidal Measurements

We study the ability of tidal signatures within the inspiral of compact binaries observed through gravitational waves (GWs) to distinguish between neutron stars (NSs) and black holes (BHs). After quantifying how hard this measurement is on a single-event basis, we investigate the ability of a large catalog of GW detections to constrain the fraction of NS in the population as a function of mass: $f_{\mathrm{NS}}(m)$. Using simulated catalogs with realistic measurement uncertainty, we find that $> O(200)$ events will be needed before we can precisely measure $f_{\mathrm{NS}}$, and catalogs of $> O(100)$ events will be needed before we can even rule out the possibility that all low-mass objects are BHs with GW data alone (i.e., without electromagnetic counterparts). Therefore, this is unlikely to occur with advanced detectors, even at design sensitivity. Nevertheless, it could be feasible with next-generation facilities like Cosmic Explorer and Einstein Telescope.

gr-qc↗

arXiv2025

Can LIGO Detect Daylight Savings Time?

Yes, it can. Catalogs produced by networks of Gravitational-wave interferometers are subject to complicated selection effects, and the gold-standard remains direct measurements of the detection probability through large injection campaigns. I leverage public data products from the LIGO-Virgo-KAGRA Collaborations' 3rd and 4th observing runs to show that there are non-trivial temporal variations within the detection probability that are well-described by a weekly cycle. There are clear differences between weekends and weekdays, between day and night (at the sites), and even between daylight-savings and standard time. I discuss possible causes for this behavior and implications.

gr-qc↗

arXiv2025

A neural network emulator of the Advanced LIGO and Advanced Virgo selection function

Characterization of search selection effects comprises a core element of gravitational-wave data analysis. Knowledge of selection effects is needed to predict observational prospects for future surveys and is essential in the statistical inference of astrophysical source populations from observed catalogs of compact binary mergers. Although gravitational-wave selection functions can be directly measured via injection campaigns -- the insertion and attempted recovery of simulated signals added to real instrumental data -- such efforts are computationally expensive. Moreover, the inability to interpolate between discrete injections limits the ability to which we can study narrow or discontinuous features in the compact binary population. For this reason, there is a growing need for alternative representations of gravitational-wave selection functions that are computationally cheap to evaluate and can be computed across a continuous range of compact binary parameters. In this paper, we describe one such representation. Using pipeline injections performed during Advanced LIGO & Advanced Virgo's third observing run (O3), we train a neural network emulator for $P(\mathrm{det}|θ)$, the probability that given a compact binary with parameters is successfully detected, averaged over the course of O3. The emulator captures the dependence of $P(\mathrm{det}|θ)$ on binary masses, spins, distance, sky position, and orbital orientation, and it is valid for compact binaries with components masses between $1$--$100\,M_\odot$. We test the emulator's ability to produce accurate distributions of detectable events, and demonstrate its use in hierarchical inference of the binary black hole population.

astro-ph.HE↗

arXiv2024

What to expect: kilonova light curve predictions via equation of state marginalization

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.

astro-ph.HE↗

arXiv2024

Striking a Chord with Spectral Sirens: multiple features in the compact binary population correlate with $H_0$

Spectral siren measurements of the Hubble constant ($H_0$) rely on correlations between observed detector-frame masses and luminosity distances. Features in the source-frame mass distribution can induce these correlations. It is crucial, then, to understand (i) which features in the source-frame mass distribution are robust against model (re)parametrization, (ii) which features carry the most information about $H_0$, and (iii) whether distinct features independently correlate with cosmological parameters. We study these questions using real gravitational-wave observations from the LIGO-Virgo-KAGRA Collaborations' third observing run. Although constraints on $H_0$ are weak, we find that current data reveals several prominent features in the mass distribution, including peaks in the binary black hole source-frame mass distribution near $\sim$ 9 $\rm{M}_{\odot}$ and $\sim$ 32$\rm{M}_{\odot}$ and a roll-off at masses above $\sim$ 46$\rm{M}_{\odot}$. For the first time using real data, we show that all of these features carry cosmological information and that the peak near $\sim$ 32$\rm{M}_{\odot}$ consistently correlates with $H_0$ most strongly. Introducing model-independent summary statistics, we show that these statistics independently correlate with $H_0$, exactly what is required to limit systematics within future spectral siren measurements from the (expected) astrophysical evolution of the mass distribution.

astro-ph.HE↗

arXiv2024

Exotic Stable Branches with Efficient TOV Sequences

Modern inference schemes for the neutron star equation of state (EoS) require large numbers of stellar models constructed with different EoS, and these stellar models must capture all the behavior of stable stars. I introduce termination conditions for sequences of stellar models for cold, non-rotating neutron stars that can identify all stable stellar configurations up to arbitrarily large central pressures along with an efficient algorithm to build accurate interpolators for macroscopic properties. I explore the behavior of stars with both high- and low-central pressures. Interestingly, I find that EoS with monotonically increasing sound-speed can produce multiple stable branches (twin stars) and that large phase transitions at high densities can produce stable branches at nearly any mass scale, including sub-solar masses, while still supporting stars with $M > 2\,M_\odot$. I conclude with some speculation about the astrophysical implications of this behavior.

astro-ph.HE↗

arXiv2024

When to Sweat the Small Stuff: identifying the most informative events from ground-based gravitational-wave detectors

We explore scaling relations for the information carried by individual events, and how that information accumulates in catalogs like those from ground-based gravitational-wave detectors. For a variety of situations, the larger number of quiet/distant signals dominates the overall information over the fewer loud/close sources, independent of how many model parameters are considered. We consider implications for a range of astrophysical scenarios, including calibration uncertainty and standard siren cosmology. However, the large number of additional events obtained by lowering the detection threshold can rapidly increase costs. We introduce a simple analysis that balances the costs of analyzing increasingly large numbers of low information events against retaining a higher threshold and running a survey for longer. With the caveat that precise cost estimates are difficult to determine, current economics favor analyzing low signal-to-noise ratio events. However, the higher detection rates expected for next-generation detectors may argue for a higher signal-to-noise ratio threshold for optimal scientific return.

gr-qc↗

arXiv2024

Theoretical and Experimental Constraints for the Equation of State of Dense and Hot Matter

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.

nucl-th↗

arXiv2024

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger searches for BNS and NSBH mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to GWs has resumed with O4. To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a MDC in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5s) and show consistency and accuracy of released data products using the MDC. For the first time, we report the expected median latency for triggers from early warning searches (-3.1s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LVK low-latency alert infrastructure and data products using the MDC and serves as a useful reference for the interpretation of O4 detections.

astro-ph.HE↗

arXiv2024

Assessing equation of state-independent relations for neutron stars with nonparametric models

Relations between neutron star properties that do not depend on the nuclear equation of state offer insights on neutron star physics and have practical applications in data analysis. Such relations are obtained by fitting to a range of phenomenological or nuclear physics equation of state models, each of which may have varying degrees of accuracy. In this study we revisit commonly-used relations and re-assess them with a very flexible set of phenomenological nonparametric equation of state models that are based on Gaussian Processes. Our models correspond to two sets: equations of state which mimic hadronic models, and equations of state with rapidly changing behavior that resemble phase transitions. We quantify the accuracy of relations under both sets and discuss their applicability with respect to expected upcoming statistical uncertainties of astrophysical observations. We further propose a goodness-of-fit metric which provides an estimate for the systematic error introduced by using the relation to model a certain equation-of-state set. Overall, the nonparametric distribution is more poorly fit with existing relations, with the I-Love-Q relations retaining the highest degree of universality. Fits degrade for relations involving the tidal deformability, such as the Binary-Love and compactness-Love relations, and when introducing phase transition phenomenology. For most relations, systematic errors are comparable to current statistical uncertainties under the nonparametric equation of state distributions.

astro-ph.HE↗

arXiv2023

DAGnabbit! Ensuring Consistency between Noise and Detection in Hierarchical Bayesian Inference

Hierarchical Bayesian inference can simultaneously account for both measurement uncertainty and selection effects within astronomical catalogs. In particular, the hierarchy imposed encodes beliefs about the interdependence of the physical processes that generate the observed data. We show that several proposed approximations within the literature actually correspond to inferences that are incompatible with any physical detection process, which can be described by a directed acyclic graph (DAG). This generically leads to biases and is associated with the assumption that detectability is independent of the observed data given the true source parameters. We show several examples of how this error can affect astrophysical inferences based on catalogs of coalescing binaries observed through gravitational waves, including misestimating the redshift evolution of the merger rate as well as incorrectly inferring that General Relativity is the correct theory of gravity when it is not. In general, one cannot directly fit for the ``detected distribution'' and ``divide out'' the selection effects in post-processing. Similarly, when comparing theoretical predictions to observations, it is better to simulate detected data (including both measurement noise and selection effects) rather than comparing estimates of the detected distributions of event parameters (which include only selection effects). While the biases introduced by model misspecification from incorrect assumptions may be smaller than statistical uncertainty for moderate catalog sizes (O(100) events), they will nevertheless pose a significant barrier to precision measurements of astrophysical populations.

gr-qc↗