arXiv subjects

Vuk Mandic

Publications and source records attributed to Vuk Mandic.

At least 19 recordsLinked to original sources

arXiv2026

Constructing likelihoods for a non-Gaussian gravitational-wave background

The stochastic gravitational-wave background from stellar-mass compact binaries is non-Gaussian since it is made up of a finite number of individual mergers. Over the years, several methods have been developed to search for this background beyond the standard cross-correlation statistic. We reformulate the data analysis problem for such astrophysical backgrounds in terms of the characteristic function of the data in multiple detectors, and show that the existing searches arise as different limiting cases. We also show that new searches can be constructed through different approximations of the characteristic function, which gives a concrete way to engineer suitable approximate sufficient statistics. To illustrate this framework in the context of a simple example, we use the formalism proposed here to analyze an exactly solvable toy model.

gr-qc↗

arXiv2026

Unifying stochastic and deterministic signals: a practical search for the intermittent binary black-hole background

Over the past decade, the Advanced LIGO, Advanced Virgo, and KAGRA detectors have detected nearly 400 compact binary mergers, most of which are stellar-mass binary black-hole (BBH) mergers. Nevertheless, most mergers in the Universe are too weak to be individually detected at current sensitivity levels. Since a BBH merger in band is much shorter than the average time between successive mergers, the population of these subthreshold events gives rise to an intermittent background of gravitational waves (GWs). This GW background is guaranteed to exist, but it has not yet been detected with current stochastic or deterministic searches. Here we propose a fast and accurate likelihood-based search for this background, which unifies the standard model-agnostic cross-correlation search and a fully deterministic, template-based search in a single likelihood. Our search is based on a truncated likelihood ratio, which is fully marginalized over the extrinsic parameters of the individual mergers, and which represents the data in two detectors as an expansion in terms of approximate sufficient statistics. These statistics are higher-order cross-moments of the data, weighted by geometrical factors that generalize the standard overlap reduction function (ORF) used in cross-correlation searches. We show that almost all the Bayesian evidence and information on the BBH merger population comes from correlations between frequency bins induced by individual mergers and is captured in the higher-order generalized ORFs. Consequently, any likelihood function that does not take these inter-frequency correlations into account is necessarily suboptimal. We validate our search on one year of simulated Advanced LIGO data, and find that a catalog-informed analysis, which uses information from both the detected and unresolved mergers, can infer the peak of the redshift distribution of the population of BBH mergers.

gr-qc↗

arXiv2026

Correcting for Evidence Uncertainty in the Templated Background Search for the Stochastic Gravitational-Wave Background

We report a new source of systematic bias in the Bayesian Templated Background Search (TBS) for the astrophysical stochastic gravitational-wave background. This bias arises from the statistical uncertainty in nested sampling evidence estimates at the segment level and persists even when the likelihood model accurately characterizes the data. Using an analytically tractable model and a frequency-domain mock data analysis with an astrophysically motivated binary black hole population, we show that this uncertainty coherently inflates the inferred duty cycle by up to an order of magnitude when millions of segments are accumulated. We propose a simple and efficient correction and demonstrate its effectiveness across all tested configurations. We also find that a search prior restricted to high optimal signal-to-noise ratio (SNR) misattributes intermediate-strength signals, while extending the prior to support weaker signals amplifies the evidence uncertainty. Both the correction and careful search prior design are essential for robust inference as TBS analyses scale to upcoming observing runs.

gr-qc↗

arXiv2026

Ultralight Bosons Explain the Mass-Spin Correlations in the Merging Binary Black Hole Population

Ultralight bosons could trigger superradiant instabilities in rapidly spinning black holes, forming oscillating clouds while extracting rotational energy. We consider an extended, superradiance-informed spin distribution model that characterizes possible environment-induced spin variations and compare its predictions with the observed population of merging black hole binaries in the Gravitational-Wave Transient Catalogs (GWTCs). We find that the mass-spin relation predicted by a scalar boson with mass $m_b\sim 10^{-12} \,\rm eV$ is consistent with the GWTCs, with increasing significance from GWTC-3.0 to 5.0. The Bayes factor reaches $\ln B \approx 7.8$ for GWTC-5.0. Intriguingly, this mass range largely coincides with a previous study based on a waveform analysis of the GW190728 gravitational wave event. Our findings provide compelling evidence that a superradiance-informed spin distribution model is highly compatible with the expanding binary black hole population dataset.

gr-qc↗

arXiv2026

Identifying Cost-Favorable Locations for Cosmic Explorer

Cosmic Explorer (CE) is a proposed next-generation gravitational-wave observatory that aims to extend our gravitational-wave vision to the edge of the observable universe. With a foundation of technology proven by the National Science Foundation's Laser Interferometer Gravitational-Wave Observatory (LIGO), CE will observe black holes and neutron stars across cosmic time, explore the nature of extreme matter with high fidelity, and probe the nature of gravity and fundamental physics. CE's reference design consists of two widely separated L-shaped detectors to be located in the conterminous United States, one with 20 km arms and one with 40 km arms. As of 2026, CE is in its design and site evaluation phase, with plans to begin observing in the early 2040s together with the Einstein Telescope in Europe. The size of CE observatories---up to an order of magnitude larger than the 4 km LIGO observatories---presents a significant challenge for identifying suitable candidate sites where CE will achieve its science goals, be built within cost boundaries, attract and retain a workforce, and align with community values. In this paper, we report on the design and use of a Python package, the Cosmic Explorer Location Search (CELS) package, to identify cost-favorable sites for CE. For a specified detector location and L-shaped geometry in the conterminous United States, CELS estimates site-preparation costs associated with excavation, land clearing, and land acquisition, while accounting for the scientific effects of detector tilt, arm length, and arm opening angle. After describing the package's methods, we present results for a national-level cost and positioning analysis that complements a recent national suitability analysis. We also discuss how future improvements to CELS will allow deeper, more local studies as the Cosmic Explorer team narrows its list of potential locations.

physics.ins-det↗

arXiv2026

All You Need is not $Ω_\mathrm{gw}$: Beyond the Mean of the Cross-Correlation Estimator when Searching for an Astrophysical Gravitational-Wave Background

Searches for the stochastic gravitational-wave background (SGWB) using ground-based detectors rely heavily on the cross-correlation estimator $\bar C_f$, whose statistical properties determine the theoretical foundation for inference frameworks built on it. Past analyses usually assume Gaussianity of $\bar C_f$, but this assumption has not been systematically tested for a background of astrophysical origin like the one produced by compact binary coalescences. In this work, we decompose $\bar C_f$ into three physically motivated components: (1) the mean intensity, (2) the geometrical shot noise, and (3) the polarization leakage, and we compute 90\% credible intervals for its cumulants up to fourth order for the binary black hole, binary neutron star, and neutron star-black hole populations by propagating local merger rate uncertainties. We find that for the upcoming LIGO O5 sensitivity, $\bar C_f$ remains effectively Gaussian for all three populations, validating current Gaussian likelihood frameworks. For Cosmic Explorer (CE), however, the $\bar C_f$ produced by binary black holes becomes non-Gaussian, with skewness and excess kurtosis of $0.31^{+0.04}_{-0.03}$ and $1.3^{+0.4}_{-0.3}$, respectively, in the most sensitive band over a one-year observation period. For binary neutron star and neutron star - black hole mergers, $\bar C_f$ remains largely Gaussian even at CE. These results indicate that the Edgeworth expansion provides an adequate leading-order description of the non-Gaussianity expected in the next-generation era, while also motivating the development of more sophisticated statistical methods for a more accurate treatment.

gr-qc↗

arXiv2026

Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly

Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixture modeling, and reversible-jump Markov chain Monte Carlo solves it exactly but mixes slowly. Learned proposals are well established at fixed dimension, but whether they can accelerate the dimension-changing moves themselves has remained largely untested. We show that the answer has a structural origin: the optimal proposal for the dimension-changing birth move is a different object in different phases of the run. While the fit is being assembled it must match the current residual - a state-dependent quantity no state-independent network can represent - but at equilibrium it degenerates to the posterior's single-component marginal, which is exactly the distribution an adaptive normalizing flow learns from the sampler's own history. A learned state-independent birth proposal is therefore useless in one phase and optimal in the other. Controlled experiments confirm the attribution: applied with an exact Metropolis--Hastings correction that leaves the target invariant for any network, the learned births leave acceptance rates unchanged yet accelerate model-order mixing - in a ten-seed benchmark they meet a pre-specified stopping rule in six of ten runs, typically several times sooner, where a strong hand-tuned baseline meets it in one (one-sided p=0.03) - and an isolation experiment shows the same flow deployed within-model buys nothing. Making no domain-specific assumptions, the same sampler counts sources in a noisy image and reconstructs signals across scientific domains, including gravitational waves from ground- and space-based detectors and a scalp EEG recording. We release the method as HyperWave, an open-source package.

physics.data-an↗

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

Searching Stochastic Gravitational Wave Background Landscape Across Frequency Bands

Gravitational wave (GW) astrophysics is entering a multi-band era with upcoming GW detectors, enabling detailed mapping of the stochastic GW background across vast frequencies. We highlight this potential via a new physics scenario: hybrid topological defects from a two-step phase transition separated by inflation. We develop a general pipeline to analyze experimental exclusions and apply it to this model. The model offers a possible explanation of the pulsar timing array signal at low frequencies, and future experiments (LISA/Cosmic Explorer/Einstein Telescope) will confirm or rule it out via the higher-frequency probes, showcasing the power of multi-band constraints.

gr-qc↗

arXiv2026

Polarized Anisotropic Stochastic Gravitational Wave Background Search with Ground-Based Detector Networks

Gravitational waves admit a Stokes decomposition into intensity ($I$), circular polarization ($V$), and linear polarization ($Q$, $U$), analogous to Cosmic Microwave Background (CMB) polarimetry. We implement a full-Stokes maximum-likelihood SGWB map-making analysis for ground-based detector networks, promoting the standard cross-correlation data products used in existing pipelines to a joint reconstruction of $I$, $V$, $Q$, $U$. Applied to LVK O3 data, we constrain the polarized angular spectra $C^{VV}_\ell$, $C^{EE}_\ell$, $C^{BB}_\ell$ and $|C^{IV}_\ell|$. We show that an intensity-only model is biased when polarized sky components are present, since the detector-network Fisher inner product does not generally make the Stokes responses orthogonal. For transient CBC foregrounds, polarized shot noise is not parametrically suppressed relative to ordinary CBC intensity shot noise. The full Stokes framework separates the Stokes sectors while providing access to polarized anisotropies invisible to conventional intensity-only searches.

gr-qc↗

arXiv2026

Parameter Estimation of the Gravitational-Wave Angular Power Spectrum in the Dirty-Map Space

We consider a search for the anisotropic stochastic gravitational-wave background (SGWB) that decomposes the sky map into its spherical harmonics components in order to obtain estimators of the angular power spectrum. Such a search often requires the inversion of a Fisher information matrix which contains small singular values. Rather than dealing with biases induced by regularization methods used to facilitate this matrix inversion, we opt to avoid this inversion step entirely by working in the so-called ``dirty map" space, and we introduce methodology for statistical model inference in this space. We apply our methodology to simulated model signals added to detector noise characterized by Advanced LIGO's third observing run and consider angular power spectra for both the SGWB auto-correlation search as well as a cross-correlation search between the SGWB and electromagnetic tracers of matter structure in the universe. In both cases we are able to reliably recover simulated model parameters for sufficiently strong signals up to maximum order spherical harmonic modes of $\ell_{max}=10$. We find the limitations of our methodology to arise from the computational cost of testing complex models, the assumption of Gaussianity of the angular power spectrum, and a cosmic variance-like source of uncertainty which scales with the strength of the underlying signal.

gr-qc↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗

arXiv2026

Importance of Shot Noise in the Search for an Isotropic Stochastic Gravitational-Wave Background with Next Generation Detectors

We investigate the impact of shot noise on the stochastic gravitational wave background generated by binary neutron star mergers, and confirm that the overall background can be significantly influenced by relatively few neighboring, loud events. To mitigate the shot noise, we propose a procedure to remove nearby events by notching them out in the time-frequency domain. Additionally, we quantify the cosmic/sample variance of the resulting background after notching, and we study the deviation between the cross-correlation measurement and the theoretical prediction of the background. Taking both effects into account, we find that the resulting sensitivity loss in the search for an isotropic background formed by binary neutron star mergers is minimal, and is limited to $\lesssim 4\%$ below 40 Hz, and to $\lesssim 1\%$ above 40 Hz.

gr-qc↗

arXiv2026

Testing models for angular power spectra: A distribution-free approach

A novel goodness-of-fit strategy is introduced for testing models of angular power spectra with unknown parameters. Using this strategy, it is possible to assess the validity of such models without specifying the distribution of the angular power spectrum estimators. This holds under general conditions, ensuring the method's applicability in diverse applications. Moreover, the proposed solution overcomes the need for case-by-case simulations when testing different models, leading to notable computational advantages.

physics.data-an↗

arXiv2026

On Validating Angular Power Spectral Models for the Stochastic Gravitational-Wave Background Without Distributional Assumptions

It is demonstrated that estimators of the angular power spectrum commonly used for the stochastic gravitational-wave background (SGWB) lack a closed-form analytical expression for the likelihood function and, typically, cannot be accurately approximated by a Gaussian likelihood. Nevertheless, a robust statistical analysis can be performed to enable the estimation and testing of angular power spectral models for the SGWB without specifying distributional assumptions. Here, the technical aspects of the method are discussed in detail. Moreover, a new, consistent estimator for the covariance of the angular power spectrum is derived. The proposed approach is applied to data from the third observing run (O3) of Advanced LIGO and Advanced Virgo.

astro-ph.IM↗

arXiv2025

Two-Step Procedure to Detect Cosmological Gravitational Wave Backgrounds with Next-Generation Terrestrial Gravitational-Wave Detectors

Cosmological gravitational-wave backgrounds are an exciting science target for next-generation ground-based detectors, as they encode invaluable information about the primordial Universe. However, any such background is expected to be obscured by the astrophysical foreground from compact-binary coalescences. We propose a novel framework to detect a cosmological gravitational-wave background in the presence of binary black holes and binary neutron star signals with next-generation ground-based detectors, including Cosmic Explorer and the Einstein Telescope. Our procedure involves first removing all the individually resolved binary black hole signals by notching them out in the time-frequency domain. Then, we perform joint Bayesian inference on the individually resolved binary neutron star signals, the unresolved binary neutron star foreground, and the cosmological background. For a flat cosmological background, we find that we can claim detection at $5\,σ$ level when $Ω_\mathrm{ref}\geqslant 2.7\times 10^{-12}/\sqrt{T_\mathrm{obs}/\mathrm{yr}}$, where $T_\mathrm{obs}$ is the observation time (in years), which is within a factor of $\lesssim2$ from the sensitivity reached in absence of these astrophysical foregrounds.

gr-qc↗

arXiv2025

Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA

The Laser Interferometer Space Antenna (LISA) will observe mHz gravitational waves from a wide variety of astrophysical sources. Of these, some will be characterizable as individual deterministic signals; the remainder will overlap to create astrophysical confusion noise. These sources of confusion noise are known as stochastic gravitational wave backgrounds (SGWBs). LISA data is expected to include several such astrophysical SGWBs, including the notable Galactic binary foreground, SGWBs from white dwarf binary populations in satellite galaxies of the Milky Way, and the SGWB from extragalactic stellar-origin binary black holes far from merger. To characterize these astrophysical signals and attempt to seek out possible underlying backgrounds of cosmological origin, it will be necessary to separate the contribution of each SGWB from that of the others. Crucially, several of these SGWBs are expected to be highly anisotropic on the sky, providing a powerful tool for spectral separation. To this end, we present BLIP 2.0: a flexible, GPU-accelerated framework for simulation and Bayesian analysis of arbitrary combinations of isotropic and anisotropic SGWBs. We leverage these capabilities to demonstrate for the first time spectral separation of the Galactic foreground, the Large Magellanic Cloud SGWB, and the SGWB from extragalactic stellar-origin binaries, and show a proof-of-concept for placing upper limits on the detection of an underlying isotropic cosmological SGWB in the presence of multiple astrophysical foregrounds.

astro-ph.IM↗