arXiv subjects

Jonathan Gair

Publications and source records attributed to Jonathan Gair.

At least 19 recordsLinked to original sources

arXiv2026

Not all those missing are lost: leveraging galaxy clustering in incomplete catalogs to unleash dark sirens cosmology

Gravitational waves offer a unique opportunity to solve the Hubble tension. In order to do so, we have to extract as much information as possible from cross correlating gravitational wave events (used as "dark sirens") with incomplete galaxy catalogs. Traditional methods assume a uniform in comoving volume distribution for galaxies missing from the catalogs, which neglects the fact that galaxies tend to cluster, leading to less precise and possibly biased posteriors. In this paper, we introduce a new method for accounting for galaxy clustering when dealing with an incomplete galaxy catalog, by adding back galaxies to the incomplete catalog and distributing them in pixels according to the correlation function $\xi(r)$. We find that our method drastically improves on traditional ones when the galaxy catalog is only $1\%-10\%$ complete, leading to posteriors that are between 2 and 4 times as precise, depending on the completeness fraction, without introducing any biases. Our method produces results comparable to traditional methods at extremely low catalog completeness fractions (<0.5\%) or for very high uncertainties in the recovered luminosity distance and sky localization of the gravitational wave events.

astro-ph.CO↗

arXiv2026

Enhancing dark siren cosmology via Gaussian process reconstruction of incomplete galaxy catalogs

We present a novel framework for improving dark siren cosmology by applying a Gaussian process (GP) to the line-of-sight (LOS) reconstruction of incomplete galaxy catalogs. In the standard galaxy catalog method for inferring the Hubble constant $H_0$ from gravitational-wave (GW) dark sirens, missing galaxies are typically assumed to follow a uniform distribution in comoving volume, an assumption that discards galaxy redshift clustering information crucial for cosmological inference. We propose instead to model the LOS galaxy redshift distribution as a non-parametric function drawn from a GP realization, which is fitted to the observed incomplete catalog via a hierarchical Bayesian likelihood that explicitly accounts for the survey selection function. Applied to mock GW and galaxy catalogs extending up to redshift $z\leq0.4$, our method yields $H_0$ constraints that are on average 23% more precise than the standard homogeneous completion when using a 24%-complete galaxy catalog, and 37% more precise for an 8%-complete catalog. The largest improvement, reaching 66%, is obtained in configurations where the GP most effectively reconstructs the redshift over- and under-density features that the homogeneous completion fails to capture.

astro-ph.CO↗

arXiv2026

A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects

Bayesian inference is commonly employed in the analysis of gravitational-wave signals not only to estimate the source parameters, but also for model selection. The latter provides insight into the physics of the source and has the potential to inform the direction for future model development. Although model comparison is usually performed by analyzing the data separately with different models and comparing the obtained Bayesian evidences, an alternative approach consists in sampling directly over the model itself. Here, we present t-roo, a reversible jump Markov chain Monte Carlo sampler capable of performing transdimensional inference on gravitational-wave signals from compact binary coalescences. Employing t-roo, a single analysis provides simultaneously the model odds ratio and the parameter posteriors for the favored models, hence yielding a potentially substantial computational advantage, particularly when comparing many models or analyzing highly informative data. t-roo is built on the sampler eryn and is specifically designed to compare models describing different kinds of sources, i.e., binary black hole, binary neutron star, or neutron star-black hole systems, as well as multiple models for the same source class. We validate the sampler on a set of injections, finding agreement with the results obtained with the nested sampler dynesty. We then use t-roo to analyze the real events GW190425 and GW230529, for which the system's parameters alone do not provide conclusive evidence of the presence of a neutron star component. t-roo can be adapted to any model-comparison scenario, thus providing a valuable tool in particular for next-generation detectors, where analyzing data separately with competing models becomes computationally even more demanding.

gr-qc↗

arXiv2026

Gotta light? Illuminating AGN disks with LISA EMRIs

We study the ability of the upcoming Laser Interferometer Space Antenna (LISA) to constrain gas torques acting on extreme-mass-ratio inspirals (EMRIs) when these are embedded in accretion disks, using recently developed relativistic models for the binary-disk interaction. Using a fully Bayesian setup, we find that, contrary to previous forecasts based on Newtonian results, these observations can provide simultaneous estimates of the disk surface density and the accretion rate (or, equivalently, its total luminosity) without the need for an electromagnetic counterpart. Our analysis also indicates that simpler measurement constraints based on the linear-signal (Fisher matrix) approximation are not valid for these systems. For typical EMRI observations, the torque amplitude can be constrained to within ~10%, strengthening the prospect of probing accretion physics at (sub)microparsec scales, deep in the strong-field gravity regime and complementing electromagnetic observations. This also strengthens LISA's ability to help answering questions such as how massive black holes grow and coevolve with their host galaxies and, by helping to identify the EMRI's host galaxy through cross-correlation with AGN catalogues, to improve the use of these sources as (dark) sirens for cosmology.

gr-qc↗

arXiv2026

Reversible-jump MCMC reveals binary black hole subpopulations with distinct redshift evolution

Analyses of the growing catalog of binary black hole (BBH) mergers observed by the LIGO-Virgo-KAGRA detectors are beginning to resolve features in their population-level mass, spin, and redshift distributions, revealing imprints of the astrophysical processes driving their formation and evolution. We present a novel method to search for subpopulations in the data using reversible-jump Markov chain Monte Carlo, providing interpretable results while making minimal prior assumptions. We find evidence for three subpopulations: a narrow subpopulation in primary mass at $\sim 10~M_\odot$ with preferentially aligned spins and unequal masses, consistent with isolated binary evolution; a subpopulation broadly distributed around $\sim 30~M_\odot$ with isotropically-distributed spins and a strong preference for equal mass ratios, consistent with dynamical formation in clusters; and a high-spin subpopulation spanning the continuum in mass, which we interpret as the confluence of multiple subdominant formation channels. When we allow for the independent redshift evolution of each subpopulation, we find that the subpopulation encompassing the $10~M_\odot$ peak evolves more quickly than the $30~M_\odot$ subpopulation, with implications for the delay-time distribution and metallicity-dependent BBH formation efficiency. Our work lays the foundation for a novel data-driven framework to infer the formation mechanisms of BBHs.

astro-ph.HE↗

arXiv2026

End-to-End Population Inference from Gravitational-Wave Strain using Transformers

The population of compact binaries encodes information about their astrophysical origins and the expansion of the universe. Hierarchical Bayesian methods infer these properties by combining single-event posteriors. As catalogs grow, however, this approach becomes computationally expensive and is subject to increasing Monte Carlo uncertainty. We introduce Dingo-Pop, a simulation-based framework that infers population posteriors directly from gravitational-wave strain data. The data for each event are embedded into low-dimensional tokens and combined using a transformer trained on simulated catalogs subject to selection effects. This enables (i) population inference without per-event Monte Carlo sampling noise, (ii) amortization across variable catalog sizes using a single network, and (iii) end-to-end inference in about one second. We train a network for catalog sizes of 25 to 1000 events, and obtain well-calibrated posteriors consistent with traditional methods. By avoiding per-event analyses that can take hours to days, Dingo-Pop enables new classes of large-scale injection studies; as an application, we examine how spectral-siren Hubble constant uncertainties change with catalog size.

gr-qc↗

arXiv2026

Pre-localization of Massive Black Hole Binaries in the Millihertz Band

The space-borne gravitational-wave (GW) detectors will open a new mass and redshift regime, allowing us to observe massive black hole binaries (MBHBs) throughout the Universe. A subset of these systems is expected to produce electromagnetic (EM) counterparts, offering a unique opportunity to follow the continuous evolution of massive black holes through joint GW and EM observations. Realizing this potential, however, requires low-latency, high-throughput data-analysis pipelines that can extract reliable source parameters and sky localizations from space-borne data streams fast enough to trigger EM follow-up. In this work we develop a fast, normalising flow-based inference pipeline designed for early-warning analysis of MBHB signals in a TianQin-like configuration. Our method combines a learned embedding of the detector time series with a neural spline flow (NSF) to perform amortized Bayesian inference, producing posterior samples for the main source parameters in roughly one minute per event. For a representative MBHB whose merger occurs $\sim 15$ minutes after the end of the analyzed GW observation, the pipeline achieves pre-merger sky localizations of order $\sim 20~\mathrm{deg}^2$, recovers the same number of sky modes as a reference parallel-tempered Markov chain Monte Carlo (PTMCMC) analysis, and yields parameter uncertainties of comparable scale, while still operating within a practically useful pre-merger warning window. These results demonstrate that NSF-based inference can deliver accurate, near-real-time parameter estimation for space-borne MBHB GW signals, and that the resulting early-warning localizations are sufficiently precise to make rapid EM follow-up.

gr-qc↗

arXiv2026

Eccentricity constraints disfavor single-single capture in nuclear star clusters as the origin of all LIGO-Virgo-KAGRA binary black holes

Multiple formation pathways have been proposed for the origin of binary black holes (BBHs). These include isolated binary evolution and dynamical assembly in dense stellar environments such as nuclear or globular star clusters. Yet, the fraction of BBHs originating from each channel remains uncertain. One way to constrain this fraction is by investigating the orbital eccentricities of the BH coalescences detected by the LIGO-Virgo-KAGRA (LVK) Collaboration. We analyze 84 BBHs from the first part of the fourth LVK observing run (O4a) using a multipolar, eccentric, aligned-spin effective-one-body waveform model. We perform parameter inference with neural posterior estimation and nested sampling. After incorporating astrophysical prior odds and comparing to the quasicircular precessing-spin hypothesis, we find that no candidates reach a high enough significance to claim a confident detection of eccentricity. We use these upper limits to explore a model, in which all O4a BBHs originate from single-single gravitational wave (GW) captures. We perform hierarchical inference on the velocity dispersion of the host environment of the BBHs and find $\sigma$ < 19.7 km/s (95% credible upper bound). This disfavors single-single capture in nuclear star clusters (approximately 20-200 km/s) as the dominant source of all observed BBH mergers. Our analysis also jointly infers the mass, spin and redshift distributions and takes into account selection effects due to using quasi-circular templates for BBH detection. Our results place improved constraints on the number of eccentric BBHs and highlight the importance of eccentricity measurements in disentangling compact-binary formation channels in current and future GW detectors.

astro-ph.HE↗

arXiv2026

Non parametric constraints of gravitational-electromagnetic luminosity distance ratio

The ratio between the gravitational waves (GW) and electromagnetic waves (EMW) luminosity distance ratio is a key observable that allows to test the nature of gravity, using gravitational waves emitted from compact binary coalescences. We develop a new non parametric method for constraining the GW-EMW distance ratio, in order to perform model independent analysis of observational data, not based on any specific theoretical of phenomenological assumption. We apply the method to the analysis of binary black hole mergers data from the GWTC-3 catalogue, performing a joint analysis of cosmological and population parameters. The results are consistent with general relativity and with previous analyses based on parametric methods.

gr-qc↗

arXiv2026

Reducing cosmological degeneracies by combining multiple classes of LISA gravitational-wave standard sirens

We present the first joint gravitational-wave cosmological inference with LISA extreme mass-ratio inspirals at $z\lesssim1$ (galaxy redshifts) and massive black hole binaries at $z\gtrsim1$ (electromagnetic counterparts). Combining these standard sirens reduces cosmological degeneracies and yields competitive constraints on the Hubble constant $H_0$ and the dark-energy equation-of-state parameter $w_0$. This highlights LISA's potential for late-time cosmology across a broad redshift range with systematics distinct from electromagnetic distance indicators.

astro-ph.CO↗

arXiv2026

Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response at fixed reference time. After sampling, we importance-sample to the true posterior based on the underlying likelihood and prior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of $\sim\!500$. At higher signal-to-noise ratios of $\sim\!1000$, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods. The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The proposed approach allows for straightforward generalizations, including a time-dependent detector response, non-stationary noise artifacts such as gaps and glitches, and low-latency parameter estimations.

astro-ph.HE↗

arXiv2026

A unified harmonic framework for dark siren cosmology

The galaxy catalog dark siren method aims to infer cosmological parameters from gravitational waves (GWs) without an electromagnetic counterpart by statistically marginalizing over possible host galaxies. The cross-correlation of GW sources and galaxies is a promising avenue for cosmological inference without requiring observed host galaxies, by leveraging 2-point statistics. We provide a detailed guide to the cross-correlation method, clarifying its relationship to standard dark siren techniques as well as the assumptions necessary to be able to use this formalism on GW data. We show that the cross-correlation method is an extension of the angular part of the galaxy catalog method in which we effectively marginalize over all possible realizations of the unknown galaxy field, jointly adding information from galaxy--galaxy clustering. Combined with the spectral sirens method, which encodes information from the GW rate evolution, mass distribution, and selection effects, one can perform an inference that leverages the joint constraining power of all dark siren methods. We also present a strategy to rigorously fold GW measurement errors into the likelihood. Using this method, we show that with a 2 Einstein Telescope + 1 Cosmic Explorer setup, the GW--galaxy cross-correlation part alone can jointly measure $H_0$ and $\Omega_{m,0}$ to 1% and 5% precision with just 2 years of data, demonstrating its potential as a precise and scalable inference technique in the next generation of GW and galaxy surveys. This is in contrast with canonical population inference techniques, which are known to scale poorly with the precision and catalog size expected of next-generation GW experiments. Contrary to some previous projections, we remain pessimistic about the cross-correlation method until these next generation detectors are online, due to its implicit requirement of large-number statistics.

astro-ph.CO↗

arXiv2026

LISA and the LISA Science Team

LISA, the Laser Interferometer Space Antenna, due to launch mid-2035, is a large class space mission by the European Space Agency (ESA). In partnership with NASA and ESA-member states, ESA is on track to launch what is expected to be the first space-based gravitational wave detector. By hosting detectors in space, one gains access to a lower frequency band of gravitational wave sources and, with them, a plethora of new science. To maximise this scientific gain, ESA and NASA selected 20 scientists for the LISA Science Team to carry out and/or lead the necessary actions leading up to LISA's launch. We give a short overview and update of the LISA mission, its science objectives and related waveforms, as well as the work of the LISA Science Team as of April 2026.

astro-ph.IM↗

arXiv2026

A framework for LISA population inference

The Laser Interferometer Space Antenna (LISA) is expected to have a source rich data stream containing signals from large numbers of many different types of source. This will include both individually resolvable signals and overlapping stochastic backgrounds, a regime intermediate between current ground-based detectors and pulsar timing arrays. The resolved sources and backgrounds will be fitted together in a high dimensional Global Fit. To extract information about the astrophysical populations to which the sources belong, we need to decode the information in the Global Fit, which requires new methodology that has not been required for the analysis of current gravitational wave detectors. Here, we start that development, presenting present a hierarchical Bayesian framework to infer the properties of astrophysical populations directly from the output of a LISA Global Fit, consistently accounting for information encoded in both the resolved sources and the unresolved background. Using a simplified model of the Global Fit, we illustrate how the interplay between resolved and unresolved components affects population inference and highlight the impact of data analysis choices, such as the signal-to-noise threshold for resolved sources, on the results. Our approach provides a practical foundation for population inference using LISA data.

gr-qc↗

arXiv2025

Flexible Gravitational-Wave Parameter Estimation with Transformers

Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge. Deep learning provides a powerful alternative to traditional inference, but existing neural models typically lack the flexibility to handle variations in data analysis settings. Such variations accommodate imperfect observations or are required for specialized tests, and could include changes in detector configurations, overall frequency ranges, or localized cuts. We introduce a flexible transformer-based architecture paired with a training strategy that enables adaptation to diverse analysis settings at inference time. Applied to parameter estimation, we demonstrate that a single flexible model, called Dingo-T1, can (i) analyze 48 gravitational-wave events from the third LIGO-Virgo-KAGRA Observing Run under a wide range of analysis configurations, (ii) enable systematic studies of how detector and frequency configurations impact inferred posteriors, and (iii) perform inspiral-merger-ringdown consistency tests probing general relativity. Dingo-T1 also improves median sample efficiency on real events from a baseline of 1.4% to 4.2%. Our approach thus demonstrates flexible and scalable inference with a principled framework for handling missing or incomplete data, key capabilities for current and next-generation observatories.

gr-qc↗

arXiv2025

Clustering effects on the Dark Siren determination of $H_0$: A simulation study

Gravitational waves (GWs) offer an alternative way to measure the Hubble parameter. The optimal technique, the ``bright siren'' approach, requires the identification of an electromagnetic counterpart. However, a significant fraction of gravitational waves signals will not have counterparts. Such events can still constrain the Hubble parameter $H_0$ via statistical methods, exploiting galaxy information from the GWs sky localisation volume. In this work, we investigate the power of this method using high-resolution, cosmological simulations that include realistic clustering. We find that clustering leads to increased convergence of the $H_0$ posteriors, with clear recovery of the input value as early as $N_{\rm gw}=40$ events, compared to uniform catalogues, where the posterior remains largely unconstrained, even with $N_{\rm gw}=100$ events. In addition, we quantify the role of catalogue incompleteness. We show that catalogues with completeness levels as low as $f=25\%$ can be competitive with fully complete catalogues, confirming the impact of clustering. Completeness levels of $f=50\%$ perform statistically similar to complete catalogues with as few as $N_{\rm gw}=40$ events. This indicates the need to focus on improving gravitational waves detection capabilities, rather than obtaining more complete galaxy catalogues. Finally, we investigate additional properties of the method by taking into consideration physical weights, different observational errors, potential biases from the $H_0$ priors, a variety of detectors' horizon distances, and different methods of catalogue completion and statistical analysis.

astro-ph.CO↗

arXiv2025

Comparing astrophysical models to gravitational-wave data in the observable space

Comparing population-synthesis models to the results of hierarchical Bayesian inference in gravitational-wave astronomy requires a careful understanding of the domain of validity of the models fitted to data. This comparison is usually done using the inferred astrophysical distribution: from the data that were collected, one deconvolves selection effects to reconstruct the generating population distribution. In this paper, we demonstrate the benefits of instead comparing observable populations directly. In this approach, the domain of validity of the models is trivially respected, such that only the relevant parameter space regions as predicted by the astrophysical models of interest contribute to the comparison. With this in mind, it can be useful to fit the observed population directly, rather than effectively deconvolving the selection effects only to fold them back in when reconstructing the observable population. We clarify that unbiased inference of the observable compact-binary population is indeed possible. Crucially, this approach still requires incorporating selection effects, but in a manner that differs from the standard implementation. We apply our observable-space reconstruction to LIGO-Virgo-KAGRA data from their third observing run and illustrate its potential by comparing the results to the predictions of a fiducial population-synthesis model.

gr-qc↗

arXiv2025

The fault in our sirens: Hierarchical diagnosis of waveform systematics in Hubble-Lema\^itre constant measurements

Cosmological inference using a population of binary black-hole mergers, combined with a galaxy catalog, presents an exciting opportunity for precision cosmology with the possibility of resolving the Hubble tension. However, the accuracy of these measurements heavily relies on the quality of the model used to infer the binary parameters, including the model of the gravitational-wave signal. We use state-of-the-art waveform models to explore the impact of inaccurate modeling in measuring the Hubble-Lema\^itre constant for the upcoming and future ground-based gravitational-wave observatories. We diagnose the presence of inaccuracies within a hierarchical population-analysis framework, without a priori knowing the true value of the parameter, by assessing the consistency of the distribution of individual posteriors in relation to their measurement errors. Our findings indicate that even a small high-mass, spin-precessing subpopulation -- comprising as little as 5\% of the population generating the events observed by the LIGO-Virgo-KAGRA Collaboration so far -- can result in an unreliable measurement of the Hubble-Lema\^itre constant in the upcoming observing runs of these detectors, with even more pronounced effects expected in future facilities on the ground.

gr-qc↗