arXiv subjects

Neil J. Cornish

Publications and source records attributed to Neil J. Cornish.

At least 37 records · Page 2Linked to original sources

arXiv2025

MaxWave: Rapid maximum likelihood wavelet reconstruction of non-Gaussian features in gravitational wave data

Advancements in the sensitivity of gravitational wave detectors have increased the detection rate of transient astrophysical signals. We improve the existing BayesWave initialization algorithm and present a rapid, low latency approximate maximum likelihood solution for reconstructing non-Gaussian features. We include three enhancements: (1) using a modified wavelet basis to eliminate redundant inner product calculations; (2) shifting from traditional time-frequency-quality factor wavelet transforms to time-frequency-time extent transforms to optimize wavelet subtractions; and (3) implementing a downsampled heterodyned wavelet transform to accelerate initial calculations. Our model can be used to denoise long-duration signals, which include the stochastic gravitational wave background from numerous unresolved sources and continuous wave signals from isolated sources such as rotating neutron stars. Through our model, we can also supplement machine learning applications that use spectrographic training data to classify and understand glitches by providing nonwhitened, time and frequency domain reconstructions of any glitch.

gr-qc↗

arXiv2025

Escaping Neal's Funnel: a multi-stage sampling method for hierarchical models

Neal's funnel refers to an exponential tapering in probability densities common to Bayesian hierarchical models. Usual sampling methods, such as Markov Chain Monte Carlo, struggle to efficiently sample the funnel. Reparameterizing the model or analytically marginalizing local parameters are common techniques to remedy sampling pathologies in distributions exhibiting Neal's funnel. In this paper, we show that the challenges of Neal's funnel can be avoided by performing the hierarchical analysis, well, hierarchically. That is, instead of sampling all parameters of the hierarchical model jointly, we break the sampling into multiple stages. The first stage samples a generalized (higher-dimensional) hierarchical model which is parameterized to lessen the sharpness of the funnel. The next stage samples from the estimated density of the first stage, but under a constraint which restricts the sampling to recover the marginal distributions on the hyper-parameters of the original (lower-dimensional) hierarchical model. A normalizing flow can be used to represent the distribution from the first stage, such that it can easily be sampled from for the second stage of the analysis. This technique is useful when effective reparameterizations are computationally expensive to calculate, or a generalized hierarchical model already exists from which it is easy to sample.

stat.ME↗

arXiv2025

Rapid inference for individual binaries and a stochastic background with pulsar timing array data

The analysis of pulsar timing array data has provided evidence for a gravitational wave background in the nanohertz band. This raises the question of what is the source of the signal, is it astrophysical or cosmological in origin? If the signal originates from a population of supermassive black hole binaries, as is generally assumed, we can expect to see evidence for both anisotropy and to be able to resolve signals from individual binaries as more data are collected. The anisotropy and resolvable systems are caused by a small number of loud signals that stand out from the crowd. Here we focus on the joint detection of individual signals and a stochastic background. While methods have previously been developed to perform such an analysis, they are currently held back by the cost of computing the joint likelihood function. Each individual source is described by $N=8+2N_p$ parameters, where $N_p$ are the number of pulsars in the array. With the latest combined datasets having over one hundred pulsars, the parameter space is very large, and consequently, it takes a large number of likelihood evaluations to explore these models. Here we present a new approach that extends the Fourier basis method, previously introduced to accelerate analyses for stochastic signals, to also include deterministic signals. Key elements of the method are that the likelihood evaluations are per-pulsar, avoiding expensive operations on large matrices, and the templates for individual binaries can be computed analytically or using fast Fourier methods on a sparsely sampled grid of time samples. This analysis method scales better than quadratically with the size of the dataset, while the approach currently being used in most analyses scales quartically or worse with the number of data points. As datasets grow with more observations, this analysis will be orders of magnitude faster than previous approaches.

gr-qc↗

arXiv2025

Uncertainty-aware waveform modeling for high signal-to-noise ratio gravitational-wave inference

Semi-analytical waveform models for black hole binaries require calibration against numerical relativity waveforms to accurately represent the late inspiral and merger, where analytical approximations fail. After the fitting coefficients contained in the model are optimized, they are typically held fixed when the model is used to infer astrophysical parameters from real gravitational-wave data. Though point estimates for the fitting parameters are adequate for most applications, they provide an incomplete description of the fit, as they do not account for either the quality of the fit or the intrinsic uncertainties in the numerical relativity data. Using the IMRPhenomD model, we illustrate how to propagate these uncertainties into the inference by sampling the fitting coefficients from a prior distribution and marginalizing over them. The prior distribution is constructed by ensuring that the model is compatible with a training set of numerical relativity surrogates, within a predefined mismatch threshold. This approach demonstrates a pathway to mitigate systematic bias in high signal-to-noise events, particularly when envisioned for the next generation of semi-analytical models.

gr-qc↗

arXiv2025

Characterizing Continuous Gravitational Waves from Supermassive Black Hole Binaries in Realistic Pulsar Timing Array Data

Pulsar timing arrays recently found evidence for a gravitational wave background (GWB), likely the stochastic overlap of GWs from many supermassive black hole binaries. Anticipating a continuous gravitational wave (CW) detection from a single binary soon to follow, we examine how well current Bayesian methods can detect CWs and characterize their binary properties by modeling the response of the NANOGrav 15-year pulsar timing array to simulated binary populations. We run Markov Chain Monte Carlo searches for CWs in these datasets and compare them to quicker detection statistics including the optimal signal-to-noise ratio (S/N), matched filter detection statistic, and reduced log-likelihood ratio between the signal and noise models calculated at the injected parameters. The latter is the best proxy for Bayesian detection fractions, corresponding to a 50% detection fraction (by Bayes factors > 10 favoring a CW detection over noise-only model) at an S/N = 4.6. Source confusion between the GWB and a CW, or between multiple CWs, can cause false detections and unexpected dismissals. 53% of realistic binary populations consistent with the recently observed GWB have successful CW detections. 82% of these CWs are in the 4th or 5th frequency bin of the 16.03 yr dataset (6.9 and 10.8 nHz), with 95th percentile regions spanning 4-12nHz frequencies, 0.7-20$\times10^9 M_\odot$ chirp masses, 60Mpc-8Gpc luminosity distances, and 18-13,000 sq. deg 68% confidence localization areas. These successful detections often poorly recover the chirp mass, with only 29% identifying the chirp mass accurately to within 1 dex with a 68% posterior width also narrower than 1 dex.

astro-ph.CO↗

arXiv2025

TDI on the fly

Space-based gravitational wave (GW) observatories, such as the future Laser Interferometer Space Antenna (LISA), employ synthetic Time Delay Interferometry (TDI) to cancel the otherwise overwhelming laser frequency noise. The phase readouts at each spacecraft are combined with a carefully designed collection of time delays that cancel the laser frequency noise. The same collection of time delays must be applied to the GW signal models used for analysis, along with geometrical factors that encode the projection of the wave polarization tensor onto the arms of the interferometer. In principle, fully generic TDI calculations require the GW signal model to be evaluated at dozens of delay times for each data sample, a process that would require tens of millions of evaluations for a year-long signal. Here, a new method is presented that cuts the computational cost by a factor of ten thousand compared to a direct implementation at the data sample cadence, while incurring no loss in accuracy. The approach works for completely general spacecraft orbits and any flavor of TDI.

gr-qc↗

arXiv2025

The NANOGrav 15 Yr Data Set: Removing Pulsars One by One from the Pulsar Timing Array

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data.

astro-ph.HE↗

arXiv2025

The NANOGrav 15-year Gravitational-Wave Background Methods

Pulsar timing arrays (PTAs) use an array of millisecond pulsars to search for gravitational waves in the nanohertz regime in pulse time of arrival data. This paper presents rigorous tests of PTA methods, examining their consistency across the relevant parameter space. We discuss updates to the 15-year isotropic gravitational-wave background analyses and their corresponding code representations. Descriptions of the internal structure of the flagship algorithms Enterprise and PTMCMCSampler are given to facilitate understanding of the PTA likelihood structure, how models are built, and what methods are currently used in sampling the high-dimensional PTA parameter space. We introduce a novel version of the PTA likelihood that uses a two-step marginalization procedure that performs much faster in gravitational wave searches, reducing the required resources facilitating the computation of Bayes factors via thermodynamic integration and sampling a large number of realizations for computing Bayesian false-alarm probabilities. We perform stringent tests of consistency and correctness of the Bayesian and frequentist analysis methods. For the Bayesian analysis, we test prior recovery, simulation recovery, and Bayes factors. For the frequentist analysis, we test that the optimal statistic, when modified to account for a non-negligible gravitational-wave background, accurately recovers the amplitude of the background. We also summarize recent advances and tests performed on the optimal statistic in the literature from both GWB detection and parameter estimation perspectives. The tests presented here validate current analyses of PTA data.

astro-ph.HE↗

arXiv2025

The NANOGrav 15 yr data set: Posterior predictive checks for gravitational-wave detection with pulsar timing arrays

Pulsar-timing-array experiments have reported evidence for a stochastic background of nanohertz gravitational waves consistent with the signal expected from a population of supermassive--black-hole binaries. Their analyses assume power-law spectra for intrinsic pulsar noise and for the background, as well as a Hellings--Downs cross-correlation pattern among the gravitational-wave--induced residuals across pulsars. These assumptions may not be realized in actuality. We test them in the NANOGrav 15 yr data set using Bayesian posterior predictive checks. After fitting our fiducial model to real data, we generate a population of simulated data-set replications. We use the replications to assess whether the optimal-statistic significance, inter-pulsar correlations, and spectral coefficients are extreme. We recover Hellings--Downs correlations in simulated data sets at significance levels consistent with the correlations measured in the NANOGrav 15 yr data set. A similar test on spectral coefficients shows that their values in real data are not extreme compared to their distributions across replications. We also evaluate the evidence for the stochastic background using posterior-predictive versions of the frequentist optimal statistic and of Bayesian model comparison, and find comparable significance (3.2 $σ$ and 3 $σ$ respectively) to what was previously reported for the standard statistics. We conclude with novel visualizations of the reconstructed gravitational waveforms that enter the residuals for each pulsar. Our analysis strengthens confidence in the identification and characterization of the gravitational-wave background.

astro-ph.HE↗

arXiv2025

The NANOGrav 15-year Data Set: Search for Gravitational Wave Memory

We present the results of a search for nonlinear gravitational wave memory in the NANOGrav 15-year data set. We find no significant evidence for memory signals in the dataset, with a maximum Bayes factor of 3.1 in favor of a model including memory. We therefore place upper limits on the strain of potential gravitational wave memory events as a function of sky location and observing epoch. We find upper limits that are not always more constraining than previous NANOGrav results. We show that it is likely due to the increase in common red noise between the 12.5-year and 15-year NANOGrav datasets.

gr-qc↗

arXiv2025

Towards robust gravitational wave detections from individual supermassive black hole binaries

The recent discovery of the stochastic gravitational-wave background via pulsar timing arrays will likely be followed by the detection of individual black hole binaries that stand out above the background. However, to confidently claim the detection of an individual binary, we need not only more and better data, but also more sophisticated analysis techniques. In this paper, we develop two new approaches that can help us more robustly ascertain if a candidate found by a search algorithm is indeed an individual supermassive black hole binary. One of these is a coherence test that directly compares the full signal model to an incoherent version of that. The other is a model scrambling approach that builds null distributions of our detection statistic and compares that with the measured value to quantify our confidence in signal coherence. Both of these rely on finding the coherence between pulsars characteristic to gravitational waves from a binary system. We test these methods on simple simulated datasets and find that they work well in correctly identifying both true gravitational waves and false positives. However, as expected for such a flexible and simple signal model, confidently identifying signal coherence is significantly harder than simply finding a candidate in most scenarios. Our analyses also indicate that the confidence with which we can identify a true signal depends not only on the signal-to-noise ratio, but also on the number of contributing pulsars and the amount of frequency evolution shown by the signal.

gr-qc↗

arXiv2025

The NANOGrav 15 yr Data Set: Running of the Spectral Index

The NANOGrav 15-year data provides compelling evidence for a stochastic gravitational-wave (GW) background at nanohertz frequencies. The simplest model-independent approach to characterizing the frequency spectrum of this signal consists in a simple power-law fit involving two parameters: an amplitude A and a spectral index γ. In this paper, we consider the next logical step beyond this minimal spectral model, allowing for a running (i.e., logarithmic frequency dependence) of the spectral index, γ_run(f) = γ+ β\ln(f/f_ref). We fit this running-power-law (RPL) model to the NANOGrav 15-year data and perform a Bayesian model comparison with the minimal constant-power-law (CPL) model, which results in a 95% credible interval for the parameter βconsistent with no running, β\in [-0.80,2.96], and an inconclusive Bayes factor, B(RPL vs. CPL) = 0.69 +- 0.01. We thus conclude that, at present, the minimal CPL model still suffices to adequately describe the NANOGrav signal; however, future data sets may well lead to a measurement of nonzero β. Finally, we interpret the RPL model as a description of primordial GWs generated during cosmic inflation, which allows us to combine our results with upper limits from big-bang nucleosynthesis, the cosmic microwave background, and LIGO-Virgo-KAGRA.

astro-ph.HE↗

arXiv2024

Fast wavelet basis search for generic gravitational wave bursts in Pulsar Timing Array data

As we move into an era of more sensitive pulsar timing array data sets, we may be able to resolve individual gravitational wave sources from the stochastic gravitational wave background. While some of these sources, like orbiting massive black hole binaries, have well-defined waveform models, there could also be signals present with unknown morphology. This motivates the search for generic gravitational-wave bursts in a signal-agnostic way. However, these searches are computationally prohibitive due to the expansive parameter space. In this paper we present QuickBurst, an algorithm with a re-defined likelihood that lets us expedite Markov chain Monte Carlo sampling for a subset of the signal parameters by avoiding repeated calculations of costly inner products. This results in an overall speedup factor of $\sim$200 on realistic simulated datasets, which is sufficient to make generic gravitational-wave burst searches feasible on current and growing pulsar timing array datasets.

gr-qc↗

arXiv2024

The NANOGrav 15 yr Data Set: Harmonic Analysis of the Pulsar Angular Correlations

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations.

astro-ph.HE↗

arXiv2024

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background

The cosmic merger history of supermassive black hole binaries (SMBHBs) is expected to produce a low-frequency gravitational wave background (GWB). Here we investigate how signs of the discrete nature of this GWB can manifest in pulsar timing arrays through excursions from, and breaks in, the expected $f_{\mathrm{GW}}^{-2/3}$ power-law of the GWB strain spectrum. To do this, we create a semi-analytic SMBHB population model, fit to NANOGrav's 15 yr GWB amplitude, and with 1,000 realizations we study the populations' characteristic strain and residual spectra. Comparing our models to the NANOGrav 15 yr spectrum, we find two interesting excursions from the power-law. The first, at $2 \; \mathrm{nHz}$, is below our GWB realizations with $p$-value significance $p = 0.05$ to $0.06$ ($\approx 1.8 σ- 1.9 σ$). The second, at $16 \; \mathrm{nHz}$, is above our GWB realizations with $p = 0.04$ to $0.15$ ($\approx 1.4 σ- 2.1 σ$). We explore the properties of a loud SMBHB which could cause such an excursion. Our simulations also show that the expected number of SMBHBs decreases by three orders of magnitude, from $\sim 10^6$ to $\sim 10^3$, between $2\; \mathrm{nHz}$ and $20 \; \mathrm{nHz}$. This causes a break in the strain spectrum as the stochasticity of the background breaks down at $26^{+28}_{-19} \; \mathrm{nHz}$, consistent with predictions pre-dating GWB measurements. The diminished GWB signal from SMBHBs at frequencies above the $26~\mathrm{nHz}$ break opens a window for PTAs to detect continuous GWs from individual SMBHBs or GWs from the early universe.

astro-ph.HE↗

arXiv2024

Waveform accuracy and systematic uncertainties in current gravitational wave observations

The post-Newtonian formalism plays an integral role in the models used to extract information from gravitational wave data, but models that incorporate this formalism are inherently approximations. Disagreement between an approximate model and nature will produce mismodeling biases in the parameters inferred from data, introducing systematic error. We here carry out a proof-of-principle study of such systematic error by considering signals produced by quasi-circular, inspiraling black hole binaries through an injection and recovery campaign. In particular, we study how unknown, but calibrated, higher-order post-Newtonian corrections to the gravitational wave phase impact systematic error in recovered parameters. As a first study, we produce injected data of non-spinning binaries as detected by a current, second-generation network of ground-based observatories and recover them with models of varying PN order in the phase. We find that the truncation of higher order (>3.5) post-Newtonian corrections to the phase can produce significant systematic error even at signal-to-noise ratios of current detector networks. We propose a method to mitigate systematic error by marginalizing over our ignorance in the waveform through the inclusion of higher-order post-Newtonian coefficients as new model parameters. We show that this method can reduce systematic error greatly at the cost of increasing statistical error.

gr-qc↗

arXiv2024

Uncovering Stealth Bias in LISA observations of Double White Dwarf Binaries due to Tidal Coupling

Double white dwarfs are important gravitational wave sources for LISA, as they are some of the most numerous compact systems in our universe. Here we consider finite-sized effects due to tidal interactions, as they are expected to have a measurable impact on these systems. Previous studies suggested that tidal effects would allow the individual masses to be measured, but there was a subtle error in those analyses. Using a fully Bayesian analysis we find that while tidal effects do not allow us to constrain the individual masses, they do yield informative lower bounds on the total mass of the system. Including tidal effects is crucial to the accuracy of our estimation of the chirp and total mass. Neglecting tidal effects leads to significant biases towards higher chirp masses, and we see that the lower bound of the total masses is biased towards a higher value as well. For many systems observed by LISA, tidal effects can lead to a "stealth" bias, since only the first derivative of the frequency can be measured. To separate tidal effects from the usual point particle decay we need to be able to measure the change in the second derivative of the frequency cause by the tides. This can only be done for high frequency systems observed with high signal-to-noise. The bias, if not accounted for, can have significant astrophysical implications; for example, it could lead to an incorrect estimation of the population of potential Type IA supernovae progenitors.

astro-ph.HE↗

arXiv2024

The NANOGrav 12.5-year data set: A computationally efficient eccentric binary search pipeline and constraints on an eccentric supermassive binary candidate in 3C 66B

The radio galaxy 3C 66B has been hypothesized to host a supermassive black hole binary (SMBHB) at its center based on electromagnetic observations. Its apparent 1.05-year period and low redshift ($\sim0.02$) make it an interesting testbed to search for low-frequency gravitational waves (GWs) using Pulsar Timing Array (PTA) experiments. This source has been subjected to multiple searches for continuous GWs from a circular SMBHB, resulting in progressively more stringent constraints on its GW amplitude and chirp mass. In this paper, we develop a pipeline for performing Bayesian targeted searches for eccentric SMBHBs in PTA data sets, and test its efficacy by applying it on simulated data sets with varying injected signal strengths. We also search for a realistic eccentric SMBHB source in 3C 66B using the NANOGrav 12.5-year data set employing PTA signal models containing Earth term-only as well as Earth+Pulsar term contributions using this pipeline. Due to limitations in our PTA signal model, we get meaningful results only when the initial eccentricity $e_0<0.5$ and the symmetric mass ratio $η>0.1$. We find no evidence for an eccentric SMBHB signal in our data, and therefore place 95% upper limits on the PTA signal amplitude of $88.1\pm3.7$ ns for the Earth term-only and $81.74\pm0.86$ ns for the Earth+Pulsar term searches for $e_0<0.5$ and $η>0.1$. Similar 95% upper limits on the chirp mass are $(1.98 \pm 0.05) \times 10^9\,M_{\odot}$ and $(1.81 \pm 0.01) \times 10^9\,M_{\odot}$. These upper limits, while less stringent than those calculated from a circular binary search in the NANOGrav 12.5-year data set, are consistent with the SMBHB model of 3C 66B developed from electromagnetic observations.

astro-ph.HE↗