arXiv subjects

Will Handley

Publications and source records attributed to Will Handley.

At least 19 recordsLinked to original sources

arXiv2026

Ab Initio Real-Time Gravitational-Wave Parameter Estimation

We present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG) structure for rapid mixing, we investigate how far we can push baseline stochastic sampling techniques on modern GPU hardware. We demonstrate that for typical long-duration binary neutron star signals observed by the LIGO and Virgo detectors, we can achieve well calibrated posterior inference on the full uncompressed data of a three detector network in a median of twelve minutes on a single GPU. This falls to five minutes when sharded across four devices. Utilising heterodyning to compress the data reduces the median wall time across an injection campaign to 89 seconds -- less than the length of the segment itself -- and enables inference with precessing spin, tidal waveforms on GW170817 in around two minutes. This pushes stochastic sampling techniques using full physical waveform calculations, launched from an uninformed prior state, towards real-time gravitational wave parameter estimation.

gr-qc↗

arXiv2026

Rapid Hubble constant inference from GW170817 using GPU-accelerated nested sampling: prior sensitivity and the limits of post-hoc reweighting

The bright-siren measurement of the Hubble constant from GW170817 (Abbott et al. 2017) assumes that switching from a volumetric to a uniform-in-$d_L$ luminosity-distance prior can be implemented by post-hoc reweighting of the baseline samples, rather than by re-running the inference under the target prior. Using a GPU-native heterodyned nested sampling pipeline that completes the full $n_{\rm live}=5000$ analysis in about 13 min on a single A100, we recompute the GW170817 $H_0$ posterior under four prior variants for the modern aligned-spin tidal waveform IMRPhenomXAS_NRTidalv3. Switching from the volumetric to a uniform-in-$d_L$ distance prior raises the high-tail probability $P(H_0>120\,\mathrm{km/s/Mpc})$ from 0.017 to 0.159 when imposed during sampling and shifts the weighted-median $H_0$ from 77.6 to 87.6 km/s/Mpc, while the binned MAP stays at 70.5 km/s/Mpc: both the tail and the bulk move under a change of prior that leaves the mode in place. Post-hoc reweighting of the baseline samples to the same target prior recovers only $P=0.041$ in the tail, approximately 17% of the directly sampled shift. The three prior variants that carry an independent nested sampling evidence agree to $\Delta\ln Z\lesssim 1.8$, so the data show at most a weak preference among the distance priors; the tail and bulk shifts are therefore properties of the prior, not a data update. Targeted mode-isolated runs reveal a $(d_L,\iota)$ bimodality whose high-$H_0$, low-$d_L$ branch (Mode B; $|\ln\mathcal{B}_{\rm B/A}|<1$) the volumetric prior assigns negligible mass: this is the mechanism behind the reweighting deficit. The reweighted posterior has a lower effective sample size than the baseline, independently flagging the coverage failure. The runtime budget makes full-sample prior-sensitivity reruns the default robustness tool for bright-siren cosmology, replacing post-hoc reweighting.

astro-ph.CO↗

arXiv2026

Numerical polology: towards next-generation model-building for cosmology

The dark sector need not be restricted to simple field content. Indeed, simple bosonic configurations, such as scalar-tensor or dark photon models, contrast with the much richer picture painted by many ultraviolet scenarios. Polology is the study of propagator poles, which correspond to particle states in any given theory. We outline a numerical polology framework for discovering perturbative, ghost-free models with consistent interactions, which produces theoretical model priors by sampling the coupling space. The method is tested on tensor field theories of up to rank three. Subsequent observational constraint pipelines are illustrated for black hole superradiance (M33 X-7), dynamical dark energy (DESI DR2, Pantheon and SH0ES) and gravitational waves (GWTC-3).

astro-ph.CO↗

arXiv2026

jaxsgp4: GPU-accelerated mega-constellation propagation with batch parallelism

As the population of anthropogenic space objects transitions from sparse clusters to mega-constellations exceeding 100,000 satellites, traditional orbital propagation techniques face a critical bottleneck. Standard CPU-bound implementations of the Simplified General Perturbations 4 (SGP4) algorithm are less well suited to handle the requisite scale of collision avoidance and Space Situational Awareness (SSA) tasks. This paper introduces \texttt{jaxsgp4}, an open-source high-performance reimplementation of SGP4 utilising the \texttt{JAX} library. \texttt{JAX} has gained traction in the landscape of computational research, offering an easy mechanism for Just-In-Time (JIT) compilation, automatic vectorisation and automatic optimisation of code for CPU, GPU and TPU hardware modalities. By refactoring the algorithm into a pure functional paradigm, we leverage these transformations to execute massively parallel propagations on modern GPUs. We demonstrate that \texttt{jaxsgp4} can propagate the entire Starlink constellation (9,341 satellites) each to 1,000 future time steps in under 4 ms on a single A100 GPU, representing a speedup of $1500\times$ over traditional C++ baselines. Furthermore, we argue that the use of 32-bit precision for SGP4 propagation tasks offers a principled trade-off, sacrificing negligible precision loss for a substantial gain in throughput on hardware accelerators.

cs.DC↗

arXiv2026

Automatic Laplace Collapsed Sampling: Scalable Marginalisation of Latent Parameters via Automatic Differentiation

We present Automatic Laplace Collapsed Sampling (ALCS), a general framework for marginalising latent parameters in Bayesian models using automatic differentiation, which we combine with nested sampling to explore the hyperparameter space in a robust and efficient manner. At each nested sampling likelihood evaluation, ALCS collapses the high-dimensional latent variables $z$ to a scalar contribution via maximum a posteriori (MAP) optimisation and a Laplace approximation, both computed using autodiff. This reduces the effective dimension from $d_\theta + d_z$ to just $d_\theta$, making Bayesian evidence computation tractable for high-dimensional settings without hand-derived gradients or Hessians, and with minimal model-specific engineering. The MAP optimisation and Hessian evaluation are parallelised across live points on GPU-hardware, making the method practical at scale. We also show that automatic differentiation enables local approximations beyond Laplace to parametric families such as the Student-$t$, which improves evidence estimates for heavy-tailed latents. We validate ALCS on a suite of benchmarks spanning hierarchical, time-series, and discrete-likelihood models and establish where the Gaussian approximation holds. This enables a post-hoc ESS diagnostic that localises failures across hyperparameter space without expensive joint sampling.

cs.LG↗

arXiv2026

Circuit Modeling for In Situ 21 cm Radiometer Calibration

Recent experiments in cosmology, particularly those aimed at detecting the faint, redshifted, global 21 cm hydrogen line (depth < ~200 mK, z > 7.5), have imposed stringent new requirements on radiometer calibration. In this work, we present a framework for circuit modeling and parameter inference to strengthen these calibration pipelines. This new approach enables in situ characterization of otherwise immeasurable systematics using physically motivated models. A combination of frequentist and Bayesian techniques are employed in a pipeline that supports iterative modeling, robust parameter estimation, and detailed uncertainty quantification. The framework is applied to the REACH telescope, where the precise correction of variations in the radio signal paths arising from component aging or environmental effects is critical. Circuit models of REACH's calibration sources are developed, with the goal of predicting source temperature corrections that are conventionally obtained from laboratory measurements. By fitting the models to measured data using a convolutional cost function, a strong agreement with RMS residuals no worse than -37 dB is obtained. However, Bayesian inference reveals that the resulting temperature corrections can have uncertainties on the order of 1 to 2 K, caused by reflection coefficient degeneracies, measurement noise, and errors in the models. To combat this, posteriors obtained from laboratory measurements are employed as updated priors, reducing correction uncertainties down to 75 mK. Ultimately, the framework provides a means of dynamically accounting for drift in system non-idealities over time, addressing the increasing precision demands of global 21 cm radio astronomy.

astro-ph.IM↗

arXiv2026

The Bayesian view of DESI DR2 with unimpeded: Evidence and tension in a combined analysis with CMB and supernovae across cosmological models

We apply the $\texttt{unimpeded}$ framework to perform a fully Bayesian reanalysis of the DESI DR2 data, using nested sampling with $\texttt{PolyChord}$ to compute evidences for $\Lambda$CDM and seven extensions across combinations of DESI DR1/DR2, Planck CMB, supernovae (Pantheon+, Union3, DES-SN5YR, DES-Dovekie), and DES-Y1 weak lensing. The Bayesian Ockham's razor penalises extended models, yielding weaker or opposite preferences compared to $\Delta\chi^2$-based analyses. For DESI DR2 BAO combined with Planck CMB alone, the DESI collaboration's $3.1\sigma$ frequentist preference for $w_0w_a$CDM is eliminated entirely: we obtain ${\ln B = -0.57{\scriptstyle\pm0.26}}$, modestly favouring $\Lambda$CDM. Adding DES-Dovekie, the recalibration of DES-SN5YR, maintains this concordance (${\ln B = -0.30{\scriptstyle\pm0.19}}$). However, when the earlier DES-SN5YR calibration is included instead, the DESI collaboration's $4.2\sigma$ result survives the Bayesian Ockham penalty as a $3.07{\scriptstyle\pm0.10},\sigma$ preference (${\ln B = +3.32{\scriptstyle\pm0.27}}$). That this signal persists despite the Ockham penalty makes the role of tension quantification essential: our analysis traced the preference to the earlier DES-SN5YR calibration, which introduced a $2.95{\scriptstyle\pm 0.04},\sigma$ conflict with DESI DR2 within $\Lambda$CDM -- a tension that stands out from the grid -- reduced to $1.96{\scriptstyle\pm 0.04},\sigma$ with the DES-Dovekie recalibration. With DES-Dovekie, the Bayesian evidence for dynamical dark energy vanishes.

astro-ph.CO↗

arXiv2026

Global structure of the time delay likelihood

We identify a fundamental pathology in the likelihood for time delay inference which challenges standard inference methods. By analysing the likelihood for time delay inference with Gaussian process light curve models, we show that it generically develops a boundary-driven "W"-shape with a global maximum at the true delay and gradual rises towards the edges of the observation window. This arises because time delay estimation is intrinsically extrapolative. In practice, global samplers such as nested sampling are steered towards spurious edge modes unless strict convergence criteria are adopted. We demonstrate this with simulations and show that the effect strengthens with higher data density over a fixed time span. To ensure convergence, we provide concrete guidance, notably increasing the number of live points. Further, we show that methods implicitly favouring small delays, for example optimisers and local MCMC, induce a bias towards larger $H_0$. Our results clarify failure modes and offer practical remedies for robust fully Bayesian time delay inference.

stat.ME↗

arXiv2026

Time delays and stationarity in quasar light curves

We present a fully Bayesian framework for time delay inference and stationarity tests in quasar light curves using marginalised Gaussian processes. The model separates a deterministic, non-stationary drift (piecewise linear mean) from stationary stochastic variability (Mat\'ern and Spectral Mixture kernels), and jointly models multiple images with per-image microlensing. Bayesian evidence and parameter posteriors are obtained via nested sampling and marginalised over model choices. Applied to the quasars WFI J2033 - 4723, B 1608 + 656, and HE 0435 - 1223, we find strong evidence for non-stationarity in B 1608 + 656 and HE 0435 - 1223, while WFI J2033 - 4723 is consistent with stationarity. The stochastic component favours an Markovian exponential kernel for B 1608 + 656 and a non-Markovian Mat\'ern-$\frac32$ kernel for WFI J2033 - 4723 and HE 0435 - 1223. Multi-length-scale Spectral Mixture kernels are disfavoured. Time delays are shown to be robust to model assumptions and consistent with prior work within the error. We further identify and mitigate a likelihood pathology which biases toward large delays, providing a practical nested sampling convergence protocol.

astro-ph.CO↗

arXiv2026

Nested Slice Sampling: Vectorized Nested Sampling for GPU-Accelerated Inference

Model comparison and calibrated uncertainty quantification often require integrating over parameters, but scalable inference can be challenging for complex, multimodal targets. Nested Sampling is a robust alternative to standard MCMC, yet its typically sequential structure and hard constraints make efficient accelerator implementations difficult. This paper introduces Nested Slice Sampling (NSS), a GPU-friendly, vectorized formulation of Nested Sampling that uses Hit-and-Run Slice Sampling for constrained updates. A tuning analysis yields a simple near-optimal rule for setting the slice width, improving high-dimensional behavior and making per-step compute more predictable for parallel execution. Experiments on challenging synthetic targets, high dimensional Bayesian inference, and Gaussian process hyperparameter marginalization show that NSS maintains accurate evidence estimates and high-quality posterior samples, and is particularly robust on difficult multimodal problems where current state-of-the-art methods such as tempered SMC baselines can struggle. An open-source implementation is released to facilitate adoption and reproducibility.

stat.CO↗

arXiv2025

PolySwyft: sequential simulation-based nested sampling

We present PolySwyft, a novel, non-amortised simulation-based inference framework that unites the strengths of nested sampling (NS) and neural ratio estimation (NRE) to tackle challenging posterior distributions when the likelihood is intractable but a forward simulator is available. By nesting rounds of NRE within the exploration of NS, and employing a principled KL-divergence criterion to adaptively terminate sampling, PolySwyft achieves faster convergence on complex, multimodal targets while rigorously preserving Bayesian validity. On a suite of toy problems with analytically known posteriors of a dim(theta,D)=(5,100) multivariate Gaussian and multivariate correlated Gaussian mixture model, we demonstrate that PolySwyft recovers all modes and credible regions with fewer simulator calls than swyft's TNRE. As a real-world application, we infer cosmological parameters dim(theta,D)=(6,111) from CMB power spectra using CosmoPower. PolySwyft is released as open-source software, offering a flexible toolkit for efficient, accurate inference across the astrophysical sciences and beyond.

astro-ph.IM↗

arXiv2025

Nested Sampling for ARIMA Model Selection in Astronomical Time-Series Analysis

The era of large-scale, high-cadence astronomical surveys demands efficient and robust methods for time-series analysis. ARIMA models provide a versatile parametric description of stochastic variability in this context. However, their practical use is limited by the challenge of selecting optimal model orders while avoiding overfitting. We present a novel solution this problem by combining Autoregressive Integrated Moving Average (ARIMA) models with the Nested Sampling algorithm. Our method yields Bayesian evidences for model comparison and also incorporates an intrinsic Occam's penalty for unnecessary model complexity. Using JAX and Blackjax, a vectorized ARIMA-Nested Sampling framework with GPU-acceleration support is implemented, allowing us to perform model selection across grids of Autoregressive (AR) and Moving Average (MA) orders, with efficient inference of selected model parameters. We validate the approach using simulated time series with known ground-truth parameters and demonstrate accurate recovery of both model order and parameters. We then apply the method to several astronomical datasets, including the historical sunspot number record, stellar light curves of KIC 12008916 and Kepler 17 from the Kepler mission, and quasar light curves of 3C 273 and S4 0954+65 from the TESS mission. For all cases, except Kepler 17, the ARIMA models selected by this method were able to accurately model the stochastic variability in the time series data as well as produce accurate multi-step ahead forecasts for the sunspot number time series. Our results demonstrate that nested sampling offers a rigorous and computationally tractable alternative to autoregressive model selection in astronomical time-series analysis.

astro-ph.IM↗

arXiv2025

A Bayesian Perspective on Evidence for Evolving Dark Energy

The DESI Collaboration reports a significant preference for a dynamic dark energy model ($w_0w_a$CDM) over the cosmological constant ($\Lambda$CDM) when their data are combined with other frontier cosmological probes. We present a direct Bayesian model comparison using nested sampling to compute the Bayesian evidence, revealing a contrasting conclusion: for the key combination of the DESI DR2 BAO and the Planck CMB data, we find the Bayesian evidence modestly favours $\Lambda$CDM (log-Bayes factor $\ln B = -0.57{\scriptstyle\pm0.26}$), in contrast to the collaboration's 3.1$\sigma$ frequentist significance in favoring $w_0w_a$CDM. Extending this analysis to also combine with the DES-SN5YR supernova catalogue, our Bayesian analysis reaches a significance of $3.07{\scriptstyle\pm0.10}\,\sigma$ in favour of $w_0w_a$CDM. By performing a comprehensive tension analysis, employing five complementary metrics, we pinpoint the origin: a significant ($2.95{\scriptstyle\pm 0.04}\,\sigma$), low-dimensional tension between DESI DR2 and DES-SN5YR that is present only within the $\Lambda$CDM framework. The $w_0w_a$CDM model is preferred precisely because its additional parameters act to resolve this specific dataset conflict. Replacing DES-SN5YR with the recalibrated DES-Dovekie dataset, this tension is reduced and the three-probe Bayesian evidence for $w_0w_a$CDM vanishes ($\ln B = -0.30{\scriptstyle\pm0.19}$). The convergence of our findings with alternative statistical analyses suggests that the preference for dynamic dark energy is primarily driven by the resolution of inter-dataset tensions, warranting a cautious interpretation of its statistical significance.

astro-ph.CO↗

arXiv2025

$\texttt{unimpeded}$: A Public Nested Sampling Database for Bayesian Cosmology

Bayesian inference is central to modern cosmology. While parameter estimation is achievable with unnormalised posteriors traditionally obtained via MCMC methods, comprehensive model comparison and tension quantification require Bayesian evidences and normalised posteriors, which remain computationally prohibitive for many researchers. To address this, we present $\texttt{unimpeded}$, a publicly available Python library and data repository providing DiRAC-funded (DP192 and 264) pre-computed nested sampling and MCMC chains with their normalised posterior samples, computed using $\texttt{Cobaya}$ and the Boltzmann solver $\texttt{CAMB}$. $\texttt{unimpeded}$ delivers systematic analysis across a grid of eight cosmological models (including $\Lambda$CDM and seven extensions) and 39 modern cosmological datasets (comprising individual probes and their pairwise combinations). The built-in tension statistics calculator enables rapid computation of six tension quantification metrics. All chains are hosted on Zenodo with permanent access via the unimpeded API, analogous to the renowned Planck Legacy Archive but utilising nested sampling in addition to traditional MCMC methods.

astro-ph.IM↗

arXiv2025

unimpeded: A Public Grid of Nested Sampling Chains for Cosmological Model Comparison and Tension Analysis

Bayesian inference is central to modern cosmology, yet comprehensive model comparison and tension quantification remain computationally prohibitive for many researchers. To address this, we release $\texttt{unimpeded}$, a publicly available Python library and data repository providing pre-computed nested sampling and MCMC chains. We apply this resource to conduct a systematic analysis across a grid of eight cosmological models, including $\Lambda$CDM and seven extensions, and 39 datasets, including individual probes and their pairwise combinations. Our model comparison reveals that whilst individual datasets show varied preferences for model extensions, the base $\Lambda$CDM model is most frequently preferred in combined analyses, with the general trend suggesting that evidence for new physics is diluted when probes are combined. Using five complementary statistics, we quantify tensions, finding the most significant to be between DES and Planck ($\sigma=3.57\pm0.10$) and SH0ES and Planck ($\sigma=3.27\pm0.10$) within $\Omega_k\Lambda$CDM. We characterise the $S_8$ tension as high-dimensional ($d_G=4.87\pm0.79$) and partially resolvable in certain extended models, whereas the Hubble tension is low-dimensional and persists across the model space. Caution should be exercised when combining datasets in tension. The $\texttt{unimpeded}$ data products, hosted on Zenodo, provide a powerful resource for reproducible cosmological analysis and underscore the robustness of the $\Lambda$CDM model against this comprehensive benchmark compilation.

astro-ph.CO↗

arXiv2025

DAMA/LIBRA and dark matter: decisive tension or contrived cancellation

The ANAIS-112 and COSINE-100 experiments were constructed to test the long-observed dark matter-like annual modulation signal reported by DAMA. While they have reported null results in their annual modulation search, it remains possible that the combined effects of quenching, efficiency, resolution and binning could transform a common nuclear recoil rate into a signal that is visible in some detectors but not others. We assess the tension between DAMA/LIBRA and these latest experiments, under a range of hypotheses ranging from physical to general parameterisations of a common nuclear recoil input. We find that, in the most physically-motivated cases, the tension between DAMA and these other NaI experiments exceeds 5$\sigma$. Lowering the tension to reasonable values requires significant tuning, such as overfitting with large numbers of free parameters, and opposite-sign modulation between recoil signals on sodium versus iodine.

hep-ph↗

arXiv2025

Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation

Inferring parameters and testing hypotheses from gravitational wave signals is a computationally intensive task central to modern astrophysics. Nested sampling, a Bayesian inference technique, has become an established standard for this in the field. However, most common implementations lack the ability to fully utilize modern hardware acceleration. In this work, we demonstrate that when nested sampling is reformulated in a natively vectorized form and run on modern GPU hardware, we can perform inference in a fraction of the time of legacy nested sampling implementations whilst preserving the accuracy and robustness of the method. This scalable, GPU-accelerated approach significantly advances nested sampling for future large-scale gravitational-wave analyses.

astro-ph.IM↗

arXiv2025

High-Dimensional Bayesian Model Comparison in Cosmology with GPU-accelerated Nested Sampling and Neural Emulators

We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosmic microwave background and cosmic shear analyses, our approach provides parameter constraints and direct calculation of Bayesian evidence. In the 39-dimensional $\Lambda$CDM vs $w_0w_a$ shear analysis, we produce Bayes factors and a robust error bar in just 2 days on a single A100 GPU, without loss of accuracy. Where CPU-based nested sampling can now be outpaced by methods relying on MCMC sampling and decoupled evidence estimation, we demonstrate that with GPU acceleration nested sampling offers the necessary speed-up to put it on equal computational footing with these methods, especially where reliable model comparison is paramount. We also explore interpolation in the matter power spectrum for cosmic shear analysis, finding a further factor of 4 speed-up with consistent posterior contours and Bayes factor. We put forward both nested and gradient-based sampling as useful tools for the modern cosmologist, where cutting-edge inference pipelines can yield orders of magnitude improvements in computation time.

astro-ph.CO↗