arXiv subjects

Joseph D. Romano

Publications and source records attributed to Joseph D. Romano.

At least 19 recordsLinked to original sources

arXiv2026

Pulsar Timing Array Sensitivity to Anisotropy: Empirical Sensitivity Curves, Scaling Relations, and the Multi-Resolution Pixel Basis

We quantify pulsar timing array (PTA) sensitivity to anisotropy in the gravitational wave background using the cross-correlation based Fisher information matrix in the pixel and spherical harmonic bases. We use a set of simulations to empirically determine scaling relations of a PTA's sensitivity to anisotropy with the number of pulsars $N_\mathrm{psr}$ in the array, the error $δt$ on the times of arrival, the frequency $f_\mathrm{GW}$ of the gravitational waves, and the angular scale $ΔΩ$ of the anisotropy. The sensitivity scales approximately as $N_\mathrm{psr}^{0.8}$, $δt^{-0.08}$, and $ΔΩ^{1.6}-ΔΩ^{2.1}$ (depending on the ranges of $\ell$ and $m$ under consideration). In addition, we use realistic simulations to project the NANOGrav PTA sensitivity to a 30-year baseline and quantify the growth in sensitivity at several timeslices. Except at the lowest frequencies, we find negligible effect on sensitivity through increasing the observation duration only. Finally, we introduce a multi-resolution pixel basis motivated by the large dependence of the sensitivity on sky location, and demonstrate the operation of the basis through a set of injections and recoveries.

astro-ph.IM↗

arXiv2026

Signal-to-Noise Ratio Contours for LISA

The Laser Interferometer Space Antenna (LISA) will search for a stochastic gravitational-wave (GW) background at millihertz frequencies, from both astrophysical and cosmological sources, and thereby open a new chapter in GW astronomy. In the literature, LISA's sensitivity to prospective GW background (GWB) signals is often quantified in terms of an expected signal-to-noise ratio (SNR) assuming perfect knowledge of the detector noise. The commonly employed expression for the SNR is, however, valid only in the limit of a weak GWB signal, which renders a large number of SNR values reported in the literature inaccurate. In this paper, we address this issue by deriving for the first time an expression for the expected optimal SNR of a LISA auto-correlation measurement that is valid at arbitrary signal strength. Based on our generalized expression, we conclude that LISA data worth an observing time of T_obs across the frequency band from f_min to f_max will never yield an SNR in excess of SNR_max = sqrt(T_obs(f_max-f_min)), which evaluates to SNR_max <~ 10^4 for typical mission parameters. We illustrate our findings in terms of generalized power-law-integrated (PLI) sensitivity curves at different SNR levels, i.e., LISA SNR contour lines in plots of the GW energy-density power spectrum. In contrast to earlier work on PLI sensitivity curves, we notably find that the LISA SNR contours are bounded from above, approximately by the LISA strain noise curve multiplied by a factor of Euler's number e. For GWB signals not much weaker than this range, the expected SNR for a LISA auto-correlation measurement needs to be evaluated based on our new expression. Our numerical results for the LISA SNR contours are available on Zenodo [https://doi.org/10.5281/zenodo.21275527].

gr-qc↗

arXiv2026

The NANOGrav 15 yr Data Set: Impacts of Customized Chromatic Noise Models on Gravitational Wave Analyses

We report updated nHz gravitational wave (GW) significance, characterization, and interpretations using the customized chromatic-noise models (CNMs) developed in Larsen, Baier et al. (2026). for the NANOGrav 15-year data set. We find increased evidence for the Hellings-Downs (HD) correlation signature of the stochastic gravitational wave background (GWB), with a Bayes factor of $1571\pm14$ for HD-correlations over a common uncorrelated red-noise process using a power-law model with $14$ Fourier modes. We find this $\sim8\times$ increase in Bayes factor from Agazie et al. (2023a) is a result of improved noise mitigation. Assuming an analytic null distribution for the frequentist interpulsar correlation statistic, this corresponds to a slightly more significant measurement from $3.16σ$ to $3.32σ$ against the no-correlation scenario. Spectral inference with CNMs brings the power-law GWB amplitude down to $A_{\rm GWB} = 2.1^{+0.6}_{-0.5}\times10^{-15}$ at fixed $γ_{\rm GWB} = 13/3$. In a varied-$γ$ analysis, the spectral index increases to $γ_{\rm GWB}=3.5^{+0.7}_{-0.6}$. We report updates on an all-sky continuous gravitational wave (CW) search as well as select targeted searches and calculate a $3.2\times$ larger detection volume for the NANOGrav detector. With CNMs, we find reduced evidence for a non-Einsteinian, scalar-transverse mode of gravity. Finally, we reinterpret the GWB first with the assumption of an astrophysical background sourced by SMBHBs and then assuming the more exotic origins of cosmic inflation, a first-order cosmological phase transition, and stable cosmic strings. Under both the SMBHB hypothesis and the cosmological hypotheses, we see only marginal shifts in model parameter posteriors which are consistent with the slightly quieter and steeper power-law GWB spectrum.

astro-ph.CO↗

arXiv2026

Orientation matters: Consequences for gravitational-wave background detectability

Cross-correlation searches for gravitational-wave backgrounds depend on the geometrical configuration (physical separation and relative orientation) of the detectors comprising the network. Applying standard techniques to a few simple examples, we illustrate how the relative orientation of a pair of Earth-based L-shaped laser interferometers can drastically impact the detectability of both isotropic and anisotropic gravitational-wave backgrounds.

gr-qc↗

arXiv2026

A thorough investigation of cross-correlation estimators for stochastic gravitational-wave background searches in ground-based detector data

Detecting a stochastic gravitational-wave background represents a crucial yet challenging objective within the field of gravitational-wave astronomy. Ground-based detectors currently rely almost exclusively on cross-correlation methods to detect stochastic gravitational-wave background signals. Traditionally, these methods define and optimize a broadband estimator initially constructed in the time domain. However, a growing number of analyses require precise narrowband estimators to accurately characterize the energy density of the underlying signal in specific frequency bins. Transitioning from time-domain broadband estimators to frequency-domain narrowband estimators introduces significant complexities that have not yet been fully explored in the existing literature. In this study, we systematically revisit and rigorously reformulate the cross-correlation method in the frequency domain, explicitly addressing and resolving issues related to non-zero covariances induced by windowing and overlapping of data in the time domain. We provide new expressions for the narrowband estimators and their covariances, which differ from those used in past searches. Fortunately, we show that the expressions that have been widely used in the field nonetheless lead to correct posterior distributions for parameter estimation and correct log-Bayes factors for model selection. By establishing a robust theoretical framework, our work facilitates more accurate and physically insightful interpretations of stochastic gravitational-wave background observations, laying an essential foundation for current and future research in this field.

gr-qc↗

arXiv2026

Gravitational Wave Measurement of the $M_\mathrm{BH}$-$M_\mathrm{bulge}$ Intrinsic Scatter at High Redshift

The observed GWB spectrum is higher in amplitude than model predictions by a factor of 2-3. Using a semi-analytic model, we evaluate the effect of a high-scatter supermassive black hole (SMBH) scaling relation ($M_\mathrm{BH}$-$M_\mathrm{bulge}$) on models of the nanohertz gravitational wave background (GWB). By implementing an intrinsic scatter of the $M_\mathrm{BH}$-$M_\mathrm{bulge}$ relation, which is larger at higher redshift, but matches local observations, we find that the amplitude of GWB models increases to be consistent with the low-frequency end of the GWB spectrum. This amplitude increase is not uniform across frequencies, a strongly evolving scatter preferentially increases the number density of the most massive SMBHs which, in the GWB spectrum, minimizes the strength of the low-frequency turnover. Our models with positively evolving intrinsic scatter can reproduce the electromagnetically observed overmassive SMBHs at $4 < z < 6$ without changing the $M_\mathrm{BH}$-$M_\mathrm{bulge}$ normalization though we find that including moderate normalization evolution marginally improves fits to the GWB data. We conclude that the $M_\mathrm{BH}$-$M_\mathrm{bulge}$ relation which best describes the available GWB and electromagnetic data sets has intrinsic scatter that evolves as $\varepsilon(z) = \varepsilon_0 + (0.56 \pm 0.4) \log_{10}(1 + z)$ and normalization that evolves as $α(z) = α_0 (1 + z)^{0.84 \pm 0.35}$. The results of this work imply that the $M_\mathrm{BH}$-$M_\mathrm{bulge}$ relation we see today is not universal throughout cosmic time and that a diversity of seeding models and growth mechanisms may be at play in the early stages of SMBH-galaxy evolution.

astro-ph.HE↗

arXiv2026

Addressing leakage and mode suppression in angular power spectrum estimation for gravitational-wave backgrounds using pulsar timing arrays

Mapping gravitational-wave background (GWB) anisotropy with pulsar timing arrays (PTAs) is affected by harmonic-space mode suppression and mode coupling arising from an array's nonuniform sky response. Spherical harmonic expansions must be truncated at finite multipole l_max^rec, often set to l_max^N_pair$\equiv {\rm int}\left[\sqrt{\text{N_pair}}-1\right]$, where N_pair is the number of distinct pulsar pairs in an array. This choice is motivated by the counting argument that cross-correlations provide at most N_pair independent constraints. We obtain the multipole l_max^res corresponding to the maximum informative angular scale of a PTA. It is defined such that expansions to l_max^res (approximately) span the space of "observable skies" encoded in the N_pair eigenmaps of the Fisher information matrix, and therefore depends on the array configuration. We explicitly show that GWB power contained in multipoles l$\gtrsim$l_max^res do not significantly affect analyses that use expansions out to l_max^res, because the PTA response acts as a low-pass filter. In contrast, truncating at l_max^rec< l_max^res leads to leakage of small-scale angular power from l_max^rec<l$\leq$l_max^res. Even choosing l_max^rec=l_max^res, the standard frequentist estimator of the angular power spectrum C_l remains biased by the modes unobservable by the array. Although we can (partially) debias the standard estimator -- improving its agreement with an injected spectrum -- this reduction in bias comes at the expense of an increase in variance, particularly for poorly constrained modes with l$\gg$l_eff. We therefore recommend: (i) using l_max^res for PTA analyses involving spherical harmonic expansions, and (ii) using the debiased standard estimator for C_l recovery, but only out to multipoles l<l_eff ($\ll$l_max^res) corresponding to sufficiently constrained modes.

gr-qc↗

arXiv2026

Kinematic Anisotropies in PTA Observations: Analytical Toolkit

The reported evidence for an isotropic gravitational-wave background (GWB) from pulsar timing array (PTA) collaborations has motivated searches for extrinsic and intrinsic anisotropies. Kinematic anisotropies may arise as a consequence of a boosted observer moving with respect to the frame in which the GWB appears isotropic. In this work, we present an analytical toolbox to describe the effects of kinematic anisotropies on the overlap reduction function. Our analytical results differ from previous findings at the quadrupole order and are detailed in three appendices. For the first time, we also derive the corresponding auto-correlation using two approaches, taking the pulsar distances to be infinite or finite, respectively. Our formulas can be used in forecasts or Bayesian analysis pipelines.

gr-qc↗

arXiv2026

Inference on inner galaxy structure via gravitational waves from supermassive binaries

The detection of a stochastic gravitational wave background by pulsar-timing arrays indicates the presence of a population of supermassive black hole binaries. Although the observed spectrum generally matches predictions for orbital evolution driven by gravitational-wave emission in circular orbits, there is a preference for a spectral turnover at the lowest observed frequencies, which may point to substantial hardening during a transition from early environmental influences to later stages dominated by emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. Here we model how the gravitational-wave spectrum depends on the initial inner galactic profile before scouring by binary ejections while accounting for a range of initial binary eccentricities. By analysing the NANOGrav 15-year data, we find that a parsec-scale galactic-centre density of around $10^6 M_{\odot} \mathrm{pc}^{-3}$ is favoured across most of the parameter space, thus shedding light on the environmental effects that shape black hole evolution and the combined matter density near galaxy centres.

astro-ph.HE↗

arXiv2026

The NANOGrav 15 yr Data Set: Piecewise Power-Law Reconstruction of the Gravitational-Wave Background

The NANOGrav 15-year (NG15) data set provides evidence for a gravitational-wave background (GWB) signal at nanohertz frequencies, which is expected to originate either from a cosmic population of inspiraling supermassive black-hole binaries or new particle physics in the early Universe. A firm identification of the source of the NG15 signal requires an accurate reconstruction of its frequency spectrum. In this paper, we provide such a spectral characterization of the NG15 signal based on a piecewise power-law (PPL) ansatz that strikes a balance between existing alternatives in the literature. Our PPL reconstruction is more flexible than the standard constant-power-law model, which describes the GWB spectrum in terms of only two parameters: an amplitude A and a spectral index gamma. Concurrently, it better approximates physically realistic GWB spectra -- especially those of cosmological origin -- than the free spectral model, since the latter allows for arbitrary variations in the GWB amplitude from one frequency bin to the next. Our PPL reconstruction of the NG15 signal relies on individual PPL models with a fixed number of internal nodes (i.e., constant power law, broken power law, doubly broken power law, etc.) that are ultimately combined in a Bayesian model average. The data products resulting from our analysis provide the basis for fast refits of spectral GWB models.

astro-ph.HE↗

arXiv2026

The NANOGrav 15 yr Data Set: Targeted Searches for Supermassive Black Hole Binaries

We present the first targeted searches for continuous gravitational waves (CWs) from 114 active galactic nuclei (AGN) that may host supermassive black hole binaries, using the NANOGrav 15 yr data set. By incorporating electromagnetic priors on sky location, distance, redshift, and CW frequency, our strain and chirp mass upper limits are typically improved by a factor of $\sim 2$ (median 2.2) relative to all-sky limits at the same frequency. Bayesian comparisons against a model including only a Hellings-Downs correlated background disfavors a CW signal for all targets, with a mean Bayes factor of $0.73 \pm 0.32$. Two targets have Bayes factors slightly above unity, but coherence tests, random targeting experiments, and a conservative accounting of the 114-target trials factor all indicate that they are consistent with noise. We use these two candidates as worked examples to illustrate an end-to-end targeted CW search analysis and a suite of follow up tests that future promising candidates would need to pass. We find that the electromagnetic interpretations of both candidates are ambiguous, and we update the constraints on a putative binary in 3C 66B, ruling out part of its previously allowed parameter space. Ultimately, our results demonstrate the current sensitivity of targeted pulsar timing array searches for CWs and define a roadmap for future multimessenger CW detections.

astro-ph.HE↗

arXiv2025

Towards symbolic regression for interpretable clinical decision scores

Medical decision-making makes frequent use of algorithms that combine risk equations with rules, providing clear and standardized treatment pathways. Symbolic regression (SR) traditionally limits its search space to continuous function forms and their parameters, making it difficult to model this decision-making. However, due to its ability to derive data-driven, interpretable models, SR holds promise for developing data-driven clinical risk scores. To that end we introduce Brush, an SR algorithm that combines decision-tree-like splitting algorithms with non-linear constant optimization, allowing for seamless integration of rule-based logic into symbolic regression and classification models. Brush achieves Pareto-optimal performance on SRBench, and was applied to recapitulate two widely used clinical scoring systems, achieving high accuracy and interpretable models. Compared to decision trees, random forests, and other SR methods, Brush achieves comparable or superior predictive performance while producing simpler models.

cs.LG↗

arXiv2025

Optimal robust detection statistics for pulsar timing arrays

Pulsar timing arrays (PTAs) seek to detect a nano-Hz stochastic gravitational-wave background (GWB) by searching for the characteristic Hellings and Downs angular pattern of timing residual correlations. So far, the evidence remains below the conventional $5$-$σ$ threshold, as assessed using the literature-standard ``optimal cross-correlation detection statistic''. While this quadratic combination of cross-correlated data maximizes the {\em deflection} (signal-to-noise ratio), it does not maximize the detection probability at fixed false-alarm probability (FAP), and therefore is not Neyman-Pearson (NP) optimal for the assumed noise and signal models. The NP-optimal detection statistic is a different quadratic form, but is not used because it also incorporates autocorrelations, making it more susceptible to uncertainties in the modeling of pulsar timing noise. Here, we derive the best compromise: a quadratic detection statistic which is as close as possible to the NP-optimal detection statistic (minimizing the variance of its difference with the NP statistic) subject to the constraint that it only uses cross-correlations, so that it is less affected by pulsar noise modeling errors. We study the performance of this new $\NPMV$ statistic for a simulated PTA whose noise and (putative) signal match those of the NANOGrav 15-year data release: GWB amplitude $A_{\rm gw}=2.1\times 10^{-15}$ and spectral index $γ=13/3$. Compared to the literature-standard ``optimal" cross-correlation detection statistic, the $\NPMV$ statistic increases the detection probability by $47\%$ when operating at a $5$-$σ$ FAP of $α= 2.9 \times 10^{-7}$.

astro-ph.IM↗

arXiv2025

Likelihoods for Stochastic Gravitational Wave Background Data Analysis

We present a systematic study of likelihood functions used for Stochastic Gravitational Wave Background (SGWB) searches. By dividing the data into many short segments, one customarily takes advantage of the Central Limit Theorem to justify a Gaussian crosscorrelation likelihood. We show, with a hierarchy of ever more realistic examples, beginning with a single frequency bin and one detector, and then moving to two and three detectors with white and colored signal and noise, that approximating the exact Whittle likelihood by various Gaussian alternatives can induce systematic biases in the estimation of the SGWB parameters. We derive several approximations for the full likelihood and identify regimes where Gaussianity breaks down. We also discuss the possibility of conditioning the full likelihood on fiducial noise estimates to produce unbiased SGWB parameter estimation. We show that for some segment durations and bandwidths, particularly in space-based and pulsar-timing arrays, the bias can exceed the statistical uncertainty. Our results provide practical guidance for segment choice, likelihood selection, and data-compression strategies to ensure robust SGWB inference in current and next-generation gravitational wave detectors.

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

Spatial and Spectral Characterization of the Gravitational-wave Background with the PTA Optimal Statistic

Pulsar timing arrays (PTAs) have made tremendous progress and are now showing strong evidence for the gravitational-wave background (GWB). Further probing the origin and characteristics of the GWB will require more generalized analysis techniques. Bayesian methods are most often used but can be computationally expensive. On the other hand, frequentist methods, like the PTA Optimal Statistic (OS), are more computationally efficient and can produce results that are complementary to Bayesian methods, allowing for stronger statistical cases to be built from a confluence of different approaches. In this work we expand the capabilities of the OS through a technique we call the Per-Frequency Optimal Statistic (PFOS). The PFOS removes the underlying power-law assumption inherent in previous implementations of the OS, and allows one to estimate the GWB spectrum in a frequency-by-frequency manner. We have also adapted a recent generalization from the OS pipeline into the PFOS, making it capable of accurately characterizing the spectrum in the intermediate and strong GW signal regimes using only a small fraction of the necessary computational resources when compared with fully-correlated Bayesian methods, while also empowering many new types of analyses not possible before. We find that even in the strong GW signal regime, where the GWB dominates over noise in all frequencies, the injected value of the signal lies within the 50th-percentile of the PFOS uncertainty distribution in 41-45% of simulations, remaining 3$σ$-consistent with unbiased estimation.

astro-ph.IM↗