Showing 1–50 of 188 results for author: Handley, W

Ab Initio Real-Time Gravitational-Wave Parameter Estimation

Authors: David Yallup, Metha Prathaban, James Alvey, Thomas C. K. Ng, Thibeau Wouters, Nikhil Sarin, Will Handley

Abstract: 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 LI… ▽ More 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. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 18 pages, 7 figures

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

Authors: Will Barker, Will Handley, Michael Hobson, Anthony Lasenby, Carlo Marzo, Alessandro Santoni, Leonardo Torcellini

Abstract: 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, gho… ▽ More 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). △ Less

Submitted 21 July, 2026; v1 submitted 29 June, 2026; originally announced June 2026.

Comments: Journal submission with community feedback incorporated (textual/reference changes)

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

Authors: Ming Han Yang, Metha Prathaban, David Yallup, Will Handley

Abstract: 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… ▽ More 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 $Δ\ln Z\lesssim 1.8$, 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 $(d_L,ι)$ 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. △ Less

Submitted 3 July, 2026; v1 submitted 29 June, 2026; originally announced June 2026.

Comments: 12 pages, 7 figures, 6 tables

Scalable Bayesian data curation for next-generation radio experiments

Authors: S. A. K. Leeney, E. de Lera Acedo, W. J. Handley, H. T. J. Bevins, G. Allen, D. Anstey, K. Artuc, G. Bernardi, M. Bucher, S. Carey, J. Cavillot, R. Chiello, A. S. Chu, W. Croukamp, J. Cumner, S. Dasgupta, A. K. Dash, D. I. L. de Villiers, J. Dhandha, A. Dragovic, J. A. Ely, A. Fialkov, T. Gessey-Jones, C. Kirkham, G. Kulkarni , et al. (20 additional authors not shown)

Abstract: Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an exter… ▽ More Next-generation radio telescopes produce data volumes that preclude manual quality assessment, yet data curation remains essential for science. We present a general, fully automatic Bayesian anomaly-detection method for radio science experiments in which data curation is performed inside the inference: a latent anomaly indicator is marginalised in the likelihood rather than converted into an external pre-flag. Implemented in JAX with GPU-accelerated inference, the pipeline assigns probabilistic data-curation scores without prior knowledge and requires no thresholds, manual inspection, or subjective decisions. We demonstrate the method on the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH), applying it to 4655 observations (one year of REACH data). The pipeline assigns scores across time and frequency, enabling identification of the optimal observations to carry forward into scientific inference while reducing the risk that contaminated data bias the result. In doing so, it simultaneously recovers weather-driven systematics, instrument-component drifts, and narrow-band radio-frequency interference, while revealing complex dependencies between data quality and environmental or instrumental state that would be difficult to uncover by manual curation alone. This turns data curation from an external manual bottleneck into autonomous, inference-level infrastructure for the Square Kilometre Array era. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 12 pages, 8 figures

Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier

Authors: S. A. K. Leeney, W. J. Handley, H. T. J. Bevins, E. de Lera Acedo

Abstract: Engineered robust losses such as Huber, Student-$t$, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupted. We introduce Neural Bayesian Anomaly Mitigation (NBAM), a general-purpose drop-in loss derived from a Bayesian latent-switch mixture model: the marginal likelihood defines a robust supervised loss, and the associated… ▽ More Engineered robust losses such as Huber, Student-$t$, and generalised cross-entropy make supervised models tolerant of contamination but cannot answer which observations are corrupted. We introduce Neural Bayesian Anomaly Mitigation (NBAM), a general-purpose drop-in loss derived from a Bayesian latent-switch mixture model: the marginal likelihood defines a robust supervised loss, and the associated posterior defines an unsupervised contamination classifier. Like Huber or Student-$t$, NBAM can replace the standard training loss in any supervised pipeline; unlike them, it additionally learns a structured contamination model and returns a calibrated per-sample contamination posterior. A learned input-dependent prior $π_φ(x)$ captures the spatial locality of contamination, so that samples near known corruptions are more likely to be flagged, while an Occam penalty emerges automatically and regularises against over-flagging. On CIFAR-10 with asymmetric label contamination, NBAM recovers the structure of the corruption process without supervision: the contamination posterior separates clean from corrupted samples, and the learned anomaly head identifies the direction of every label-flip pair. Alongside these capabilities, NBAM outperforms the four robust-loss baselines considered here at contamination rates 0.2-0.6. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 13 pages, 4 figures

Optimisation of calibration sources for global 21-cm experiments: the REACH case

Authors: Adarsh Kumar Dash, Dominic Anstey, Harry T. J. Bevins, Eloy de Lera Acedo, Gary Allen, Kaan Artuc, Gianni Bernardi, Martin Bucher, Steve Carey, Jean Cavillot, Ricardo Chiello, Adelicia S. Chu, Wessel Croukamp, John Cumner, Saswata Dasgupta, Dirk I. L. de Villiers, Jiten Dhandha, Aleksandra Dragovic, John A. Ely, Anastasia Fialkov, Thomas Gessey-Jones, Will J. Handley, Christian Kirkham, Girish Kulkarni, Samuel A. K. Leeney , et al. (19 additional authors not shown)

Abstract: The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that coul… ▽ More The spin-flip 21-cm signal from the Cosmic Dawn and the Epoch of Reionization is an essential probe of the conditions that led to the formation of the first luminous objects in the early Universe. However, its detection remains a major challenge owing to its low strength compared to the bright foregrounds and the requirement of precise calibration of the instrument to prevent systematics that could hinder a detection or lead to false inferences. REACH (Radio Experiment for the Analysis of Cosmic Hydrogen) is a radiometer experiment designed to detect this sky-averaged signal in the frequency range of 50--130~MHz. Using a wide-beam antenna, REACH calibration relies on internal reference sources, covering a broad range of temperatures and reflection coefficients. The choice of type and number of calibrators used significantly influences the quality of the calibration. This work investigates these effects and introduces a novel method for selecting an optimal set of calibration sources. With an optimised set, we aim to reduce calibration time, thereby increasing sky integration time while preserving calibration accuracy. We explore two optimisation strategies: one applied across the full receiver band and another performed on a frequency-by-frequency basis. Finally, we demonstrate that, with a total calibration time comparable to the conventional full-calibrator set, an optimised set with fewer calibrators achieves approximately a $15~\%$ reduction in calibrated temperature noise and improved absolute calibration of the instrument. This has implications for better calibration strategies in similar radiometer experiments. △ Less

Submitted 31 March, 2026; originally announced April 2026.

Comments: 13 pages, 8 figures

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

Authors: Charlotte Priestley, Will Handley

Abstract: 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 Awar… ▽ More 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. △ Less

Submitted 29 March, 2026; originally announced March 2026.

Comments: 11 pages, 3 figures

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

Authors: Toby Lovick, David Yallup, Will Handley

Abstract: 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… ▽ More 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 $d_θ+ d_z$ to just $d_θ$, 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. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: 28 Pages, 7 Figures. Comments welcome

Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation

Authors: S. A. K. Leeney, T. Gessey-Jones, W. J. Handley, E. de Lera Acedo, H. T. J. Bevins, J. L. Tutt

Abstract: For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Estimation (cNBRE), which extends neural Bayes ratio estimation by conditioning on design parameters, enabling a single trained network to estimate Bayes factors across a continuous design space. Applied to 21-cm radio cosm… ▽ More For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Estimation (cNBRE), which extends neural Bayes ratio estimation by conditioning on design parameters, enabling a single trained network to estimate Bayes factors across a continuous design space. Applied to 21-cm radio cosmology with simulations representative of the REACH experiment, the amortised nature of cNBRE enables systematic design space exploration that would be intractable with traditional point-wise methods, while recovering established physical relationships. The analysis demonstrates a ~20 percentage point variation in detection probability with antenna orientation for a single night of observation, a design decision that would be trivial to implement if determined prior to antenna construction. This framework enables efficient, globally-informed experimental design optimisation for a wide range of scientific applications. △ Less

Submitted 27 March, 2026; originally announced March 2026.

Comments: 11 pages, 5 figures. Submitted to IEEE Transactions on Neural Networks and Learning Systems

Circuit Modeling for In Situ 21 cm Radiometer Calibration

Authors: Gary Vincent Charles Allen, Saurabh Pegwal, Dirk de Villiers, Dominic Anstey, Kaan Artuc, Harry Bevins, Gianni Bernardi, Martin Bucher, Steve Carey, Jean Cavillot, Ricardo Chiello, Adele Chu, Wessel Croukamp, John Cumner, Ardash Dash, Saswata Dasgupta, Eloy de Lera Acedo, Jiten Dhandha, Aleksandra Dragovic, John Ely, Anastasia Fialkov, Thomas Gessey-Jones, Will Handley, Christian Kirkham, Girish Kulkarny , et al. (20 additional authors not shown)

Abstract: 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 other… ▽ More 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. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Comments: 29 pages, 16 figures, 2 tables, authored by the REACH collaboration, Preprint of an article submitted for consideration in Journal of Astronomical Instrumentation \c{opyright} 2026 copyright World Scientific Publishing Company https://www.worldscientific.com/worldscinet/jai

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

Authors: Dily Duan Yi Ong, David Yallup, Will Handley

Abstract: 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 $Λ$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… ▽ More 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 $Λ$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 $Δχ^2$-based analyses. For DESI DR2 BAO combined with Planck CMB alone, the DESI collaboration's $3.1σ$ frequentist preference for $w_0w_a$CDM is eliminated entirely: we obtain ${\ln B = -0.57{\scriptstyle\pm0.26}}$, modestly favouring $Λ$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 $4.2σ$ result survives the Bayesian Ockham penalty as a $3.07{\scriptstyle\pm0.10},σ$ 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 $2.95{\scriptstyle\pm 0.04},σ$ conflict with DESI DR2 within $Λ$CDM -- a tension that stands out from the grid -- reduced to $1.96{\scriptstyle\pm 0.04},σ$ with the DES-Dovekie recalibration. With DES-Dovekie, the Bayesian evidence for dynamical dark energy vanishes. △ Less

Submitted 15 May, 2026; v1 submitted 5 March, 2026; originally announced March 2026.

Comments: 30 pages, 15 figures

Global structure of the time delay likelihood

Authors: Namu Kroupa, Will Handley

Abstract: 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 b… ▽ More 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. △ Less

Submitted 28 April, 2026; v1 submitted 25 February, 2026; originally announced February 2026.

Comments: 21 pages, 8 figures

Journal ref: Phys. Rev. Research 8, 023175 (2026)

Time delays and stationarity in quasar light curves

Authors: Namu Kroupa, David Yallup, Will Handley

Abstract: 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 (Matérn and Spectral Mixture kernels), and jointly models multiple images with per-image microlensing. Bayesian evidence and parame… ▽ More 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 (Matérn 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 Matérn-$\frac32$ 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. △ Less

Submitted 27 February, 2026; v1 submitted 11 February, 2026; originally announced February 2026.

Comments: 18 pages, 4 figures

Journal ref: Phys. Rev. D 113, 063549 (2026)

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

Authors: David Yallup, Namu Kroupa, Will Handley

Abstract: 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),… ▽ More 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. △ Less

Submitted 10 May, 2026; v1 submitted 30 January, 2026; originally announced January 2026.

Comments: 58 pages, 13 figures, Accepted to Transactions on Machine Learning Research

Journal ref: Transactions on Machine Learning Research, 2026

PolySwyft: sequential simulation-based nested sampling

Authors: Kilian H. Scheutwinkel, Will Handley, Christoph Weniger, Eloy de Lera Acedo

Abstract: 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 adapti… ▽ More 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. △ Less

Submitted 9 December, 2025; originally announced December 2025.

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

Authors: Ajinkya Naik, Will Handley

Abstract: 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 Autoreg… ▽ More 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. △ Less

Submitted 10 June, 2026; v1 submitted 1 December, 2025; originally announced December 2025.

Comments: 20 pages, 31 figures; submitted to MNRAS

A Bayesian Perspective on Evidence for Evolving Dark Energy

Authors: Dily Duan Yi Ong, David Yallup, Will Handley

Abstract: The DESI Collaboration reports a significant preference for a dynamic dark energy model ($w_0w_a$CDM) over the cosmological constant ($Λ$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 th… ▽ More The DESI Collaboration reports a significant preference for a dynamic dark energy model ($w_0w_a$CDM) over the cosmological constant ($Λ$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 $Λ$CDM (log-Bayes factor $\ln B = -0.57{\scriptstyle\pm0.26}$), in contrast to the collaboration's 3.1$σ$ 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 $3.07{\scriptstyle\pm0.10}\,σ$ in favour of $w_0w_a$CDM. By performing a comprehensive tension analysis, employing five complementary metrics, we pinpoint the origin: a significant ($2.95{\scriptstyle\pm 0.04}\,σ$), low-dimensional tension between DESI DR2 and DES-SN5YR that is present only within the $Λ$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. △ Less

Submitted 12 May, 2026; v1 submitted 13 November, 2025; originally announced November 2025.

Comments: 5 pages, 1 figure, 1 table, version 2 typographical correction to prior and neutrino setup

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

Authors: Dily Duan Yi Ong, Will Handley

Abstract: 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 av… ▽ More 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 $Λ$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. △ Less

Submitted 7 November, 2025; originally announced November 2025.

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

Authors: Dily Duan Yi Ong, Will Handley

Abstract: 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 gri… ▽ More 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 $Λ$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 $Λ$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 ($σ=3.57\pm0.10$) and SH0ES and Planck ($σ=3.27\pm0.10$) within $Ω_kΛ$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 $Λ$CDM model against this comprehensive benchmark compilation. △ Less

Submitted 10 June, 2026; v1 submitted 6 November, 2025; originally announced November 2025.

Comments: 49 pages, 13 figures. Version 3: Revised in response to the JCAP editor's report. Added Section 3.2.12 on the treatment of nuisance parameters in the evidence and tension calculations. Results unchanged

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

Authors: Giorgio Busoni, Jonathan M. Cornell, Will Handley, Felix Kahlhoefer, Anders Kvellestad, Masen Pitts, Lauren Street, Aaron C. Vincent, Martin White

Abstract: 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 detec… ▽ More 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$σ$. 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. △ Less

Submitted 24 June, 2026; v1 submitted 6 October, 2025; originally announced October 2025.

Comments: Cool new figure. Accepted for publication

Report number: ADP-25-32/T1294,TTP25-033, gambit-physics-2025

Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation

Authors: David Yallup, Metha Prathaban, James Alvey, Will Handley

Abstract: 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 sa… ▽ More 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. △ Less

Submitted 29 September, 2025; originally announced September 2025.

Comments: To be submitted to SciPost Physics Proceedings (EuCAIFCon 2025)

Bayesian Anomaly Detection for Ia Cosmology: Automating SALT3 Data Curation

Authors: S. A. K. Leeney, W. J. Handley, H. T. J. Bevins, E. de Lera Acedo

Abstract: Precision cosmology with Type Ia supernovae (SNe Ia) requires robust quality control of large, heterogeneous datasets. Current data processing often relies on manual, subjective rejection of photometric data, a practice that is not scalable for forthcoming surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We present a Bayesian framework that automates this step b… ▽ More Precision cosmology with Type Ia supernovae (SNe Ia) requires robust quality control of large, heterogeneous datasets. Current data processing often relies on manual, subjective rejection of photometric data, a practice that is not scalable for forthcoming surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We present a Bayesian framework that automates this step by integrating anomaly detection directly into the light curve fitting process. While the framework is model-agnostic and compatible with any spectral energy distribution (SED) model, here we demonstrate its application with the SALT3 model, implemented fully on GPU using \texttt{JAX-bandflux} for computational efficiency. Our method models the probability of each photometric measurement being anomalous with respect to the model, simultaneously inferring the physical supernova parameters and each data point's posterior probability of being a contaminant. Applying this framework to the Hawaii Supernova Flows dataset, we demonstrate its three principal capabilities: (i) robust mitigation of isolated outliers; (ii) automated identification and rejection of entirely corrupted bandpasses; and (iii) preservation of valid data by flagging only specific anomalous points within otherwise usable filters. We also find that contaminants are systematically brighter and bluer, which if uncorrected could bias subsequent cosmological inference. △ Less

Submitted 16 September, 2025; originally announced September 2025.

Comments: 14 pages, 8 figures

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

Authors: Toby Lovick, David Yallup, Davide Piras, Alessio Spurio Mancini, Will Handley

Abstract: 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 $Λ$CDM vs $w_0w_a$ shear analysis, we produce Bayes factor… ▽ More 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 $Λ$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. △ Less

Submitted 6 October, 2025; v1 submitted 16 September, 2025; originally announced September 2025.

Comments: 12 pages 5 figures. Updated to add clarity in the main results table and detail added throughout. Comments welcome

Dynamic or Systematic? Bayesian model selection between dark energy and supernova biases

Authors: A. N. Ormondroyd, W. J. Handley, M. P. Hobson, A. N. Lasenby, D. Yallup

Abstract: The Dark Energy Survey 5-year (DES-5Y) supernovae, combined with Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations, appear to favour Chevallier-Polarski-Linder $(w_0, w_a)$ dynamical dark energy over $Λ$CDM. It has been suggested in other work that this is driven by a systematic in the DES pipeline, which particularly affects the low-redshift supernovae brought in from legac… ▽ More The Dark Energy Survey 5-year (DES-5Y) supernovae, combined with Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillations, appear to favour Chevallier-Polarski-Linder $(w_0, w_a)$ dynamical dark energy over $Λ$CDM. It has been suggested in other work that this is driven by a systematic in the DES pipeline, which particularly affects the low-redshift supernovae brought in from legacy surveys. It is difficult to investigate these data in isolation, however, as the complicated supernovae pipelines must properly account for selection effects. In this work, we discover that a magnitude offset between the low- and high-redshift supernovae is favoured by the Bayesian evidence over the flexknot dark energy found in our previous work. In addition, we find that the possible tension between DES-5Y and DESI is significantly reduced by such an offset. The recent DES-Dovekie recalibration partially alleviates this tension, but does not eliminate it. We also take the opportunity to trial Nested Bridge Sampling with Sequential Monte Carlo as an alternative method for calculating Bayes factors. △ Less

Submitted 29 July, 2026; v1 submitted 16 September, 2025; originally announced September 2025.

Comments: 10 pages, 11 figures, 2 tables. Updated to match MNRAS publication, including an update in light of DES-Dovekie, which released since the original upload. Comments welcome!

Journal ref: Mon Not R Astron Soc (2026)

CMB Constraints on Quantized Spatial Curvature $Ω_K$ in globally CPT-symmetric universes

Authors: Wei-Ning Deng, Will Handley

Abstract: The periodic solution of the Friedmann equation in conformal time, implies that only cosmological perturbations exhibiting corresponding symmetries are physically permissible, leading to a discrete spectrum of allowed wave vectors. Furthermore, in a spatially closed universe, these wave vectors are independently constrained to be integers. Matching these two distinct quantization conditions provid… ▽ More The periodic solution of the Friedmann equation in conformal time, implies that only cosmological perturbations exhibiting corresponding symmetries are physically permissible, leading to a discrete spectrum of allowed wave vectors. Furthermore, in a spatially closed universe, these wave vectors are independently constrained to be integers. Matching these two distinct quantization conditions provides a novel theoretical constraint on the possible values of spatial curvature. In this work, we numerically solve the cosmological perturbation equations, incorporating radiation anisotropy and higher-order Boltzmann terms, to calculate these discrete wave vectors with improved precision. Subsequently, we generate Cosmic Microwave Background (CMB) power spectra for different characteristic spacings of these quantized wave vectors. Finally, we apply the constraint to Planck 2018 observational data to determine the cosmological parameters. This analysis yields a discrete set of allowed values for the spatial curvature, $Ω_K$, including $[-0.076,-0.039, -0.024, -0.016, -0.012, \dots]$. △ Less

Submitted 12 September, 2025; originally announced September 2025.

Comments: 8 pages, 4 figures

Gravitational-wave inference at GPU speed: A bilby-like nested sampling kernel within blackjax-ns

Authors: Metha Prathaban, David Yallup, James Alvey, Ming Yang, Will Templeton, Will Handley

Abstract: We present a GPU-accelerated implementation of the gravitational-wave Bayesian inference pipeline for parameter estimation and model comparison. Specifically, we implement the `acceptance-walk' sampling method, a cornerstone algorithm for gravitational-wave inference within the bilby and dynesty framework. By integrating this trusted kernel with the vectorized blackjax-ns framework, we achieve typ… ▽ More We present a GPU-accelerated implementation of the gravitational-wave Bayesian inference pipeline for parameter estimation and model comparison. Specifically, we implement the `acceptance-walk' sampling method, a cornerstone algorithm for gravitational-wave inference within the bilby and dynesty framework. By integrating this trusted kernel with the vectorized blackjax-ns framework, we achieve typical speedups of 20-40x for aligned spin binary black hole analyses, while recovering posteriors and evidences that are statistically identical to the original CPU implementation. This faithful re-implementation of a community-standard algorithm establishes a foundational benchmark for gravitational-wave inference. It quantifies the performance gains attributable solely to the architectural shift to GPUs, creating a vital reference against which future parallel sampling algorithms can be rigorously assessed. This allows for a clear distinction between algorithmic innovation and the inherent speedup from hardware. Our work provides a validated community tool for performing GPU-accelerated nested sampling in gravitational-wave data analyses. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: 13 total pages, 15 total figures (main text: 11 pages, 12 figures; appendix: 2 pages, 3 figures)

Alleviating the Hubble tension with Torsion Condensation (TorC)

Authors: Sinah Legner, Will Handley, Will Barker, Adam Ormondroyd

Abstract: Constraints on the cosmological parameters of Torsion Condensation (TorC) are investigated using Planck 2018 Cosmic Microwave Background data. TorC is a case of Poincaré gauge theory -- a formulation of gravity motivated by the gauge field theories underlying fundamental forces in the standard model of particle physics. Unlike general relativity, TorC incorporates intrinsic torsion degrees of free… ▽ More Constraints on the cosmological parameters of Torsion Condensation (TorC) are investigated using Planck 2018 Cosmic Microwave Background data. TorC is a case of Poincaré gauge theory -- a formulation of gravity motivated by the gauge field theories underlying fundamental forces in the standard model of particle physics. Unlike general relativity, TorC incorporates intrinsic torsion degrees of freedom while maintaining second-order field equations. At specific parameter values, it reduces to the $Λ$CDM model, providing a natural extension to standard cosmology. The base model of TorC introduces two parameters beyond those in $Λ$CDM: the initial value of the torsion scalar field and its time derivative -- one can absorb the latter by allowing the dark energy density to float. To constrain these parameters, `PolyChord` nested sampling algorithm is employed, interfaced via `Cobaya` with a modified version of `CAMB`. Our results indicate that TorC allows for a larger inferred Hubble constant, offering a potential resolution to the Hubble tension. Tension analysis using the $R$-statistic shows that TorC alleviates the statistical tension between the Planck 2018 and SH0Es 2020 datasets, though this improvement is not sufficient to decisively favour TorC over $Λ$CDM in a Bayesian model comparison. This study highlights TorC as a compelling theory of gravity, demonstrating its potential to address cosmological tensions and motivating further investigations of extended theories of gravity within a cosmological context. As current and upcoming surveys -- including Euclid, Roman Space Telescope, Vera C. Rubin Observatory, LISA, and Simons Observatory -- deliver data on gravity across all scales, they will offer critical tests of gravity models like TorC, making the present a pivotal moment for exploring extended theories of gravity. △ Less

Submitted 2 April, 2026; v1 submitted 12 July, 2025; originally announced July 2025.

Comments: 26 pages (main text: 15 pages), 13 figures, 7 tables, version published in JCAP03(2026)003, comments welcome!

Radiometer Calibration using Machine Learning

Authors: S. A. K. Leeney, H. T. J. Bevins, E. de Lera Acedo, W. J. Handley, C. Kirkham, R. S. Patel, J. Zhu, D. Molnar, J. Cumner, D. Anstey, K. Artuc, G. Bernardi, M. Bucher, S. Carey, J. Cavillot, R. Chiello, W. Croukamp, D. I. L. de Villiers, J. A. Ely, A. Fialkov, T. Gessey-Jones, G. Kulkarni, A. Magro, P. D. Meerburg, S. Mittal , et al. (13 additional authors not shown)

Abstract: Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver… ▽ More Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line. △ Less

Submitted 23 April, 2025; originally announced April 2025.

Comments: Under peer review for publication in Nature Scientific Reports as part of the Radio Astronomy collection

Resonances in reflective Hamiltonian Monte Carlo

Authors: Namu Kroupa, Gábor Csányi, Will Handley

Abstract: In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialised from a Dirac delta distribution and the uniform distribution is targeted. By quantifying the instantaneous non-uniformity of the distribution with the Sinkhorn divergence, we elucidate the principal mechanisms underlying the mixing problems. In spheres and… ▽ More In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialised from a Dirac delta distribution and the uniform distribution is targeted. By quantifying the instantaneous non-uniformity of the distribution with the Sinkhorn divergence, we elucidate the principal mechanisms underlying the mixing problems. In spheres and cubes, we show that the collective motion transitions between fluid-like and discretisation-dominated behaviour, with the critical step size scaling as a power law in the dimension. In both regimes, the particles can spontaneously unmix, leading to resonances in the particle density and the aforementioned problems. Additionally, low-dimensional toy models of the dynamics are constructed which reproduce the dominant features of the high-dimensional problem. Finally, the dynamics is contrasted with the exact Hamiltonian particle flow and tuning practices are discussed. △ Less

Submitted 16 April, 2025; originally announced April 2025.

Journal ref: Phys. Rev. E 111, 045308 (2025)

Extracting cosmological information from the abundance of galaxy clusters with simulation-based inference

Authors: Íñigo Zubeldia, Boris Bolliet, Anthony Challinor, William Handley

Abstract: The abundance of galaxy clusters as a function of mass and redshift is a well-established and powerful cosmological probe. Cosmological analyses based on galaxy cluster number counts have traditionally relied on explicitly computed likelihoods, which are often challenging to develop with the required accuracy and expensive to evaluate. In this work, we implement an alternative approach based on si… ▽ More The abundance of galaxy clusters as a function of mass and redshift is a well-established and powerful cosmological probe. Cosmological analyses based on galaxy cluster number counts have traditionally relied on explicitly computed likelihoods, which are often challenging to develop with the required accuracy and expensive to evaluate. In this work, we implement an alternative approach based on simulation-based inference (SBI) methods that relies solely on synthetic galaxy cluster catalogues generated under a given model. These catalogues are much easier to produce than it is to develop and validate a likelihood. We validate this approach in the context of the galaxy cluster survey of the upcoming Simons Observatory for a setup in which we can also evaluate an exact explicit likelihood. We find that our SBI-based approach yields cosmological parameter posterior means that are within $0.2\,σ$ of those obtained with the explicit likelihood and with biases smaller than $0.1\,σ$. We also introduce and validate a procedure to assess the goodness of fit using only synthetic catalogues similar to those used for training. This demonstrates, for the first time, that a galaxy cluster number count cosmological analysis can be performed fully without resorting to a likelihood at any stage. Finally, we apply our SBI-based approach to the real Planck MMF3 cosmology sample, obtaining cosmological parameter constraints that are within $0.1\,σ$ of their likelihood-based counterparts. This constitutes the first SBI-based number count cosmological analysis of a real galaxy cluster catalogue. △ Less

Submitted 14 April, 2025; originally announced April 2025.

Comments: 13 pages, 3 figures

Rapid and Late Cosmic Reionization Driven by Massive Galaxies: a Joint Analysis of Constraints from 21-cm, Lyman Line & CMB Data Sets

Authors: Peter H. Sims, Harry T. J. Bevins, Anastasia Fialkov, Dominic Anstey, Will J. Handley, Stefan Heimersheim, Eloy de Lera Acedo, Rajesh Mondal, Rennan Barkana

Abstract: Observations of the Epoch of Reionization (EoR) have the potential to answer long-standing questions of astrophysical interest regarding the nature of the first luminous sources and their effects on the intergalactic medium (IGM). We present astrophysical constraints from a Neural Density Estimation-Accelerated Bayesian joint analysis of constraints deriving from Cosmic Microwave Background power… ▽ More Observations of the Epoch of Reionization (EoR) have the potential to answer long-standing questions of astrophysical interest regarding the nature of the first luminous sources and their effects on the intergalactic medium (IGM). We present astrophysical constraints from a Neural Density Estimation-Accelerated Bayesian joint analysis of constraints deriving from Cosmic Microwave Background power spectrum measurements from Planck and SPT, IGM neutral fraction measurements from Lyman-line-based data sets and 21-cm power spectrum upper limits from HERA, LOFAR and the MWA. In the context of the model employed, the data is found to be consistent with galaxies forming from predominantly atomic-cooled hydrogen gas in dark matter halos, with masses $M_\mathrm{min} \gtrsim 2.6 \times 10^{9}~M_{\odot} ((1+z)/10)^{\frac{1}{2}}$ at 95% credibility ($V_\mathrm{c} \gtrsim 50~\mathrm{km~s^{-1}}$) being the dominant galactic population driving reionization. These galaxies reionize the neutral hydrogen in the IGM over a narrow redshift interval ($Δz_\mathrm{re} < 1.8$ at 95% credibility), with the midpoint of reionization (when the sky-averaged IGM neutral fraction is 50%) constrained to $z_{50} = 7.16^{+0.15}_{-0.12}$. Given the parameter posteriors from our joint analysis, we find that the posterior predictive distribution of the global 21-cm signal is reduced in amplitude and shifted to lower redshifts relative to the model prior. We caution, however, that our inferences are model-dependent. Future work incorporating updated, mass-dependent star formation efficiencies in atomic cooling halos, informed by the latest UV luminosity function constraints from the James Webb Space Telescope, promises to refine these inferences further and enhance our understanding of cosmic reionization. △ Less

Submitted 13 April, 2025; originally announced April 2025.

Comments: 24 pages, 11 figures; submitted for publication in MNRAS

Journal ref: Mon Not R Astron Soc (2025) 3856-3882

The Bayesian Global Sky Model (B-GSM): A Calibrated Low Frequency Sky Model for EoR Applications

Authors: George Carter, Will Handley, Mark Ashdown, Nima Razavi-Ghods

Abstract: We present the Bayesian Global Sky Model (B-GSM), a new absolutely calibrated model of the diffuse Galactic foreground at frequencies below 408 MHz. We assemble a dataset of publicly available diffuse emission maps at frequencies between 45 MHz and 408 MHz, along with absolute temperature data from the EDGES radiometer between 40 and 200 MHz. We use nested sampling to perform a joint Bayesian anal… ▽ More We present the Bayesian Global Sky Model (B-GSM), a new absolutely calibrated model of the diffuse Galactic foreground at frequencies below 408 MHz. We assemble a dataset of publicly available diffuse emission maps at frequencies between 45 MHz and 408 MHz, along with absolute temperature data from the EDGES radiometer between 40 and 200 MHz. We use nested sampling to perform a joint Bayesian analysis of these two datasets and determine posterior distributions of: spatially resolved diffuse components, spectral parameters for the diffuse emission, and calibration corrections for each observed map. Using Bayesian model comparison, we find that the low-frequency sky is optimally modelled by two emission components, each following a curved power-law spectrum. The spectrum for the first component has a spectral index of beta_1 = -2.633 plus/minus 0.002 and a curvature of gamma_1 = 0.014 plus/minus 0.001, while the second has beta_2 = -2.108 plus/minus 0.008 and gamma_2 = -0.424 plus/minus 0.008. The diffuse maps require temperature-scale corrections of 1% to 29%, and zero-level adjustments of a few kelvin to a few hundred kelvin. We find that the Haslam 408 MHz map is well calibrated, requiring a scale correction of 1.029 plus/minus 0.003 (about 3%) and a zero-level correction of 0.91 plus/minus 0.05 kelvin. Posterior predictions for the sky's absolute temperature are in excellent agreement with EDGES data, indicating accurate calibration. The posterior sky predictions agree with observations within statistical uncertainty across all frequencies. However, agreement varies by position, with the largest discrepancies in the Galactic plane. This is the second paper in the B-GSM series; the low-frequency sky model, along with all code and data, is available for download. △ Less

Submitted 6 April, 2025; originally announced April 2025.

The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics

Authors: Eleonora Di Valentino, Jackson Levi Said, Adam Riess, Agnieszka Pollo, Vivian Poulin, Adrià Gómez-Valent, Amanda Weltman, Antonella Palmese, Caroline D. Huang, Carsten van de Bruck, Chandra Shekhar Saraf, Cheng-Yu Kuo, Cora Uhlemann, Daniela Grandón, Dante Paz, Dominique Eckert, Elsa M. Teixeira, Emmanuel N. Saridakis, Eoin Ó Colgáin, Florian Beutler, Florian Niedermann, Francesco Bajardi, Gabriela Barenboim, Giulia Gubitosi, Ilaria Musella , et al. (516 additional authors not shown)

Abstract: The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-t… ▽ More The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged] △ Less

Submitted 4 August, 2025; v1 submitted 2 April, 2025; originally announced April 2025.

Comments: 416 pages, 81 figures

Journal ref: Phys. Dark Univ. 49 (2025) 101965

Comparison of dynamical dark energy with ΛCDM in light of DESI DR2

Authors: A. N. Ormondroyd, W. J. Handley, M. P. Hobson, A. N. Lasenby

Abstract: We present an updated reconstruction of the dark energy equation of state, $w(a)$, using the newly released DESI DR2 Baryon Acoustic Oscillation (BAO) data in combination with Pantheon+ and DES5Y Type Ia supernovae measurements, respectively. Building on our previous analysis in arXiv:2503.08658, which employed a nonparametric flexknot reconstruction approach, we examine whether the evidence for d… ▽ More We present an updated reconstruction of the dark energy equation of state, $w(a)$, using the newly released DESI DR2 Baryon Acoustic Oscillation (BAO) data in combination with Pantheon+ and DES5Y Type Ia supernovae measurements, respectively. Building on our previous analysis in arXiv:2503.08658, which employed a nonparametric flexknot reconstruction approach, we examine whether the evidence for dynamical dark energy persists with the improved precision of the DESI DR2 dataset. We find that while the overall qualitative structure of $w(a)$ remains consistent with our earlier findings, the statistical support for dynamical dark energy is reduced when considering DESI DR2 data alone, particularly for more complex flexknot models with higher numbers of knots. However, the evidence for simpler dynamical models, such as $w$CDM and CPL (which correspond to $n=1$ and $n=2$ knots respectively), increases relative to $Λ$CDM with DESI DR2 alone, with CPL being the preferred dynamical model, consistent with previous DESI analyses. When combined with Pantheon+ data, the conclusions remain broadly consistent with our earlier work, but when instead combined with DES5Y supernovae data, there is an increased preference for flexknot models for all values of $n$ considered. This results in all such models being preferred over $Λ$CDM, with the CPL model being the most favoured by a Bayes factor of $\sim 2.3$ relative to $Λ$CDM. △ Less

Submitted 31 July, 2025; v1 submitted 21 March, 2025; originally announced March 2025.

Comments: 5 pages, 5 figures, 1 table. Updated to match MNRAS submission

On the accuracy of posterior recovery with neural network emulators

Authors: H. T. J. Bevins, T. Gessey-Jones, W. J. Handley

Abstract: Neural network emulators are widely used in astrophysics and cosmology to approximate complex simulations inside Bayesian inference loops. Ad hoc rules of thumb are often used to justify the emulator accuracy required for reliable posterior recovery. We provide a theoretically motivated limit on the maximum amount of incorrect information inferred by using an emulator with a given accuracy. Under… ▽ More Neural network emulators are widely used in astrophysics and cosmology to approximate complex simulations inside Bayesian inference loops. Ad hoc rules of thumb are often used to justify the emulator accuracy required for reliable posterior recovery. We provide a theoretically motivated limit on the maximum amount of incorrect information inferred by using an emulator with a given accuracy. Under assumptions of linearity in the model, uncorrelated noise in the data and a Gaussian likelihood function, we demonstrate that the difference between the true underlying posterior and the recovered posterior can be quantified via a Kullback-Leibler divergence. We demonstrate how this limit can be used in the field of 21-cm cosmology by comparing the posteriors recovered when fitting mock data sets generated with the 1D radiative transfer code ARES directly with the simulation code and separately with an emulator. This paper is partly in response to and builds upon recent discussions in the literature which call into question the use of emulators in Bayesian inference pipelines. Upon repeating some aspects of these analyses, we find these concerns quantitatively unjustified, with accurate posterior recovery possible even when the mean RMSE error for the emulator is approximately 20% of the magnitude of the noise in the data. For the purposes of community reproducibility, we make our analysis code public at this link https://github.com/htjb/validating_posteriors. △ Less

Submitted 24 February, 2026; v1 submitted 17 March, 2025; originally announced March 2025.

Comments: Updated to be in line with the journal

Journal ref: Mon Not R Astron Soc (2025) 375-390

Nonparametric reconstructions of dynamical dark energy via flexknots

Authors: A. N. Ormondroyd, W. J. Handley, M. P. Hobson, A. N. Lasenby

Abstract: Recent cosmological surveys have provided unprecedented datasets that can be used to reconstruct the history of the dark energy equation of state. In this work, a free-form "flexknot'' parameterisation is employed to represent $w(a)$ as a linear spline between free-moving nodes, the number of which may vary. By combining DESI Baryon Acoustic Oscillation measurements with Pantheon+ or DES5Y superno… ▽ More Recent cosmological surveys have provided unprecedented datasets that can be used to reconstruct the history of the dark energy equation of state. In this work, a free-form "flexknot'' parameterisation is employed to represent $w(a)$ as a linear spline between free-moving nodes, the number of which may vary. By combining DESI Baryon Acoustic Oscillation measurements with Pantheon+ or DES5Y supernovae, the functional posteriors of $w(a)$ reveal an unexpected W-shaped structure. While the Bayesian evidence may still favour $Λ$CDM, the robustness of these results suggests the structure is indeed present in the data. The tension $R$-statistic and suspiciousness have been marginalised over models, and demonstrate that while the reconstructions from DESI and Pantheon+ agree, DESI and DES5Y do not. We conclude that, while there is no smoking gun for dynamical dark energy, the structure unearthed in this work is generally too complex to be captured by the restrictive $w$CDM or CPL parameterisations. △ Less

Submitted 29 April, 2025; v1 submitted 11 March, 2025; originally announced March 2025.

Comments: 8+5 pages, 9+3 figures, 1 table. Corrected error in the calculation of Bayes factors for the n=1 (wCDM) flexknot model. Nested sampling chains and python code to be released soon!

Journal ref: Mon Not R Astron Soc (2025) 3388-3400

The mass distribution of the first stars can be determined via the 21-cm signal

Authors: T. Gessey-Jones, N. S. Sartorio, H. T. J. Bevins, A. Fialkov, W. J. Handley, E. de Lera Acedo, G. M. Mirouh, R. G. Izzard, R. Barkana

Abstract: The formation of the first stars and the subsequent population of X-ray binaries represents a fundamental transition in the state of the Universe as it evolves from near homogeneity to being abundant in collapsed structures such as galaxies. Due to a lack of direct observations, the properties of these stars remain highly uncertain. By considering the impact of the first stars and their remnant X-… ▽ More The formation of the first stars and the subsequent population of X-ray binaries represents a fundamental transition in the state of the Universe as it evolves from near homogeneity to being abundant in collapsed structures such as galaxies. Due to a lack of direct observations, the properties of these stars remain highly uncertain. By considering the impact of the first stars and their remnant X-ray binaries on the cosmological 21-cm signal, we demonstrate that upcoming observations have the potential to significantly improve our understanding of these objects. We find a 25 mK sensitivity measurement of the 21-cm global signal by a wide-beam radiometer, such as REACH, or 3,000 hours of foreground avoidance observations of the 21-cm power spectrum by SKA-Low, could provide three-sigma constraints on the mass distribution of the first stars. Such measurements will fill a critical gap in our understanding of the early Universe and aid in interpreting high-redshift galaxy observations. △ Less

Submitted 25 February, 2025; originally announced February 2025.

Comments: 17+7 pages, 2+5 figures; Nature Astronomy (2025)

Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference

Authors: Nicolas Mediato-Diaz, Will Handley

Abstract: We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for the posterior distributions of hyper-parameters of the linear likelihood in terms of samples drawn from a simulator, for both uniform and conjugate priors. This m… ▽ More We develop the framework of Linear Simulation-based Inference (LSBI), an application of simulation-based inference where the likelihood is approximated by a Gaussian linear function of its parameters. We obtain analytical expressions for the posterior distributions of hyper-parameters of the linear likelihood in terms of samples drawn from a simulator, for both uniform and conjugate priors. This method is applied sequentially to several toy-models and tested on emulated datasets for the Cosmic Microwave Background temperature power spectrum. We find that convergence is achieved after four or five rounds of $\mathcal{O}(10^4)$ simulations, which is competitive with state-of-the-art neural density estimation methods. Therefore, we demonstrate that it is possible to obtain significant information gain and generate posteriors that agree with the underlying parameters while maintaining explainability and intellectual oversight. △ Less

Submitted 7 January, 2025; originally announced January 2025.

Comments: 12 pages, 7 figures

The Bayesian Global Sky Model (B-GSM): Validation of a Data Driven Bayesian Simultaneous Component Separation and Calibration Algorithm for EoR Foreground Modelling

Authors: George Carter, Will Handley, Mark Ashdown, Nima Razavi-Ghods

Abstract: We introduce the Bayesian Global Sky Model (B-GSM), a novel data-driven Bayesian approach to modelling radio foregrounds at frequencies <400~MHz. B-GSM aims to address the limitations of previous models by incorporating robust error quantification and calibration. Using nested sampling, we compute Bayesian evidence and posterior distributions for the spectral behaviour and spatial amplitudes of di… ▽ More We introduce the Bayesian Global Sky Model (B-GSM), a novel data-driven Bayesian approach to modelling radio foregrounds at frequencies <400~MHz. B-GSM aims to address the limitations of previous models by incorporating robust error quantification and calibration. Using nested sampling, we compute Bayesian evidence and posterior distributions for the spectral behaviour and spatial amplitudes of diffuse emission components. Bayesian model comparison is used to determine the optimal number of emission components and their spectral parametrisation. Posterior sky predictions are conditioned on both diffuse emission and absolute temperature datasets, enabling simultaneous component separation and calibration. B-GSM is validated against a synthetic dataset designed to mimic the partial sky coverage, thermal noise, and calibration uncertainties present in real observations of the diffuse sky at low frequencies. B-GSM correctly identifies a model parametrisation with two emission components featuring curved power-law spectra. The posterior sky predictions agree with the true synthetic sky within statistical uncertainty. We find that the root-mean-square (RMS) residuals between the true and posterior predictions for the sky temperature as a function of LST are significantly reduced, when compared to the uncalibrated dataset. This indicates that B-GSM is able to correctly calibrate its posterior sky prediction to the independent absolute temperature dataset. We find that while the spectral parameters and component amplitudes exhibit some sensitivity to prior assumptions, the posterior sky predictions remain robust across a selection of different priors. This is the first of two papers, and is focused on validation of B-GSMs Bayesian framework, the second paper will present results of deployment on real data and introduce the low-frequency sky model which will be available for public download. △ Less

Submitted 2 January, 2025; originally announced January 2025.

Journal ref: Mon Not R Astron Soc (2025) 1463-1487

Accounting for Noise and Singularities in Bayesian Calibration Methods for Global 21-cm Cosmology Experiments

Authors: Christian J. Kirkham, William J. Handley, Jiacong Zhu, Kaan Artuc, Ian L. V. Roque, Samuel A. K. Leeney, Harry T. J. Bevins, Dominic J. Anstey, Eloy de Lera Acedo

Abstract: Due to the large dynamic ranges involved with separating the cosmological 21-cm signal from the Cosmic Dawn from galactic foregrounds, a well-calibrated instrument is essential to avoid biases from instrumental systematics. In this paper we present three methods for calibrating a global 21-cm cosmology experiment using the noise wave parameter formalisation to characterise a low noise amplifier in… ▽ More Due to the large dynamic ranges involved with separating the cosmological 21-cm signal from the Cosmic Dawn from galactic foregrounds, a well-calibrated instrument is essential to avoid biases from instrumental systematics. In this paper we present three methods for calibrating a global 21-cm cosmology experiment using the noise wave parameter formalisation to characterise a low noise amplifier including a careful consideration of how calibrator temperature noise and singularities will bias the result. The first method presented in this paper builds upon the existing conjugate priors method by weighting the calibrators by a physically motivated factor, thereby avoiding singularities and normalising the noise. The second method fits polynomials to the noise wave parameters by marginalising over the polynomial coefficients and sampling the polynomial orders as parameters. The third method introduces a physically motivated noise model to the marginalised polynomial method. Running these methods on a suite of simulated datasets based on the REACH receiver design and a lab dataset, we found that our methods produced a calibration solution which is equally as or more accurate than the existing conjugate priors method when compared with an analytic estimate of the calibrator's noise. We find in the case of the measured lab dataset the conjugate priors method is biased heavily by the large noise on the shorted load calibrator, resulting in incorrect noise wave parameter fits. This is mitigated by the methods introduced in this paper which calibrate the validation source spectra to within 5% of the noise floor. △ Less

Submitted 22 January, 2025; v1 submitted 18 December, 2024; originally announced December 2024.

Comments: 9 pages, 5 figures

Journal ref: Mon Not R Astron Soc (2025) 4312-4320

Accelerated nested sampling with posterior repartitioning and $β$-flows for gravitational waves

Authors: Metha Prathaban, Harry Bevins, Will Handley

Abstract: There is an ever-growing need in the gravitational wave community for fast and reliable inference methods, accompanied by an informative error bar. Nested sampling satisfies the last two requirements, but its computational cost can become prohibitive when using the most accurate waveform models. In this paper, we demonstrate the acceleration of nested sampling using a technique called posterior re… ▽ More There is an ever-growing need in the gravitational wave community for fast and reliable inference methods, accompanied by an informative error bar. Nested sampling satisfies the last two requirements, but its computational cost can become prohibitive when using the most accurate waveform models. In this paper, we demonstrate the acceleration of nested sampling using a technique called posterior repartitioning. This method leverages nested sampling's unique ability to separate prior and likelihood contributions at the algorithmic level. Specifically, we define a `repartitioned prior' informed by the posterior from a low-resolution run. To construct this repartitioned prior, we use a $β$-flow, a novel type of conditional normalizing flow designed to better learn deep tail probabilities. $β$-flows are trained on the entire nested sampling run and conditioned on an inverse temperature $β$. Applying our methods to simulated and real binary black hole mergers, we demonstrate how they can reduce the number of likelihood evaluations required for a given evidence precision by up to an order of magnitude, enabling faster model comparison and parameter estimation. Furthermore, we highlight the robustness of using $β$-flows over standard normalizing flows for posterior repartitioning. Notably, $β$-flows are able to recover posteriors and evidences which are generally consistent with those from traditional nested sampling, even in cases where standard normalizing flows fail. △ Less

Submitted 4 November, 2025; v1 submitted 26 November, 2024; originally announced November 2024.

Comments: Version published in MNRAS

On the spatial distribution of the Large-Scale structure: An Unsupervised search for Parity Violation

Authors: Samuel Hewson, Will J. Handley, Christopher G. Lester

Abstract: We use machine learning methods to search for parity violations in the Large-Scale Structure (LSS) of the Universe, motivated by recent claims of chirality detection using the 4-Point Correlation Function (4PCF), which would suggest new physics during the epoch of inflation. This work seeks to reproduce these claims using methods originating from high energy collider analyses. Our machine learning… ▽ More We use machine learning methods to search for parity violations in the Large-Scale Structure (LSS) of the Universe, motivated by recent claims of chirality detection using the 4-Point Correlation Function (4PCF), which would suggest new physics during the epoch of inflation. This work seeks to reproduce these claims using methods originating from high energy collider analyses. Our machine learning methods optimise some underlying parity odd function of the data, and use it to evaluate the parity odd fraction. We demonstrate the effectiveness and suitability of these methods and then apply them to the Baryon Oscillation Spectroscopic Survey (BOSS) catalogue. No parity violation is detected at any significance. △ Less

Submitted 10 May, 2025; v1 submitted 21 October, 2024; originally announced October 2024.

Comments: 21 pages, 15 figures. Accepted for publication in PRD, updated to accepted version

Journal ref: Phys. Rev. D 111, 123528 (2025)

A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications

Authors: Joshua Albert, Csaba Balazs, Andrew Fowlie, Will Handley, Nicholas Hunt-Smith, Roberto Ruiz de Austri, Martin White

Abstract: For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide range of Markov Chain Monte Carlo (MCMC) and nested sampling techniques to determine their relative efficacy on functions that resemble those encountered… ▽ More For several decades now, Bayesian inference techniques have been applied to theories of particle physics, cosmology and astrophysics to obtain the probability density functions of their free parameters. In this study, we review and compare a wide range of Markov Chain Monte Carlo (MCMC) and nested sampling techniques to determine their relative efficacy on functions that resemble those encountered most frequently in the particle astrophysics literature. Our first series of tests explores a series of high-dimensional analytic test functions that exemplify particular challenges, for example highly multimodal posteriors or posteriors with curving degeneracies. We then investigate two real physics examples, the first being a global fit of the $Λ$CDM model using cosmic microwave background data from the Planck experiment, and the second being a global fit of the Minimal Supersymmetric Standard Model using a wide variety of collider and astrophysics data. We show that several examples widely thought to be most easily solved using nested sampling approaches can in fact be more efficiently solved using modern MCMC algorithms, but the details of the implementation matter. Furthermore, we also provide a series of useful insights for practitioners of particle astrophysics and cosmology. △ Less

Submitted 24 November, 2024; v1 submitted 27 September, 2024; originally announced September 2024.

Comments: 45 pages, 8 figures, 20 tables

Towards a Field Based Bayesian Evidence Inference from Nested Sampling Data

Authors: Margret Westerkamp, Jakob Roth, Philipp Frank, Will Handley, Torsten Enßlin

Abstract: Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propose to transform the prior volume estimation into a Bayesian inference problem, which allows us to incorporate a smoothness assumption for likelihood-pr… ▽ More Nested sampling (NS) is a stochastic method for computing the log-evidence of a Bayesian problem. It relies on stochastic estimates of prior volumes enclosed by likelihood contours, which limits the accuracy of the log-evidence calculation. We propose to transform the prior volume estimation into a Bayesian inference problem, which allows us to incorporate a smoothness assumption for likelihood-prior volume relations. As a result, we aim to increase the accuracy of the volume estimates and thus improve the overall log-evidence calculation using NS. The method presented works as a post-processing step for NS and provides posterior samples of the likelihood-prior-volume relation, from which the log-evidence can be calculated. We demonstrate an implementation of the algorithm and compare its results with plain NS on two synthetic datasets for which the underlying evidence is known. We find a significant improvement in accuracy for runs with less than one hundred active samples in NS, but are prone to numerical problems beyond this point. △ Less

Submitted 19 August, 2024; originally announced August 2024.

Predicting spatial curvature $Ω_K$ in globally $CPT$-symmetric universes

Authors: Wei-Ning Deng, Will Handley

Abstract: Boyle and Turok's $CPT$-symmetric universe model posits that the universe was symmetric at the Big Bang, addressing numerous problems in both cosmology and the Standard Model of particle physics. We extend this model by incorporating the symmetric conditions at the end of the Universe, which impose constraints on the allowed perturbation modes. These constrained modes conflict with the integer wav… ▽ More Boyle and Turok's $CPT$-symmetric universe model posits that the universe was symmetric at the Big Bang, addressing numerous problems in both cosmology and the Standard Model of particle physics. We extend this model by incorporating the symmetric conditions at the end of the Universe, which impose constraints on the allowed perturbation modes. These constrained modes conflict with the integer wave vectors required by the global spatial geometry in a closed universe. To resolve this conflict, only specific values of curvature are permissible, and in particular the curvature density is constrained to be $Ω_K \in \{-0.014, -0.009, -0.003, \ldots\}$, consistent with Planck observations. △ Less

Submitted 13 August, 2024; v1 submitted 25 July, 2024; originally announced July 2024.

Comments: 5 pages, 5 figures

Calibrating Bayesian Tension Statistics using Neural Ratio Estimation

Authors: Harry T. J. Bevins, William J. Handley, Thomas Gessey-Jones

Abstract: When fits of the same physical model to two different datasets disagree, we call this tension. Several apparent tensions in cosmology have occupied researchers in recent years, and a number of different metrics have been proposed to quantify tension. Many of these metrics suffer from limiting assumptions, and correctly calibrating these is essential if we want to successfully determine whether dis… ▽ More When fits of the same physical model to two different datasets disagree, we call this tension. Several apparent tensions in cosmology have occupied researchers in recent years, and a number of different metrics have been proposed to quantify tension. Many of these metrics suffer from limiting assumptions, and correctly calibrating these is essential if we want to successfully determine whether discrepancies are significant. A commonly used metric of tension is the evidence ratio R. The statistic has been widely adopted by the community as a Bayesian way of quantifying tensions, however, it has a non-trivial dependence on the prior that is not always accounted for properly. We show that this can be calibrated out effectively with Neural Ratio Estimation. We demonstrate our proposed calibration technique with an analytic example, a toy example inspired by 21-cm cosmology, and with observations of the Baryon Acoustic Oscillations from the Dark Energy Spectroscopic Instrument (DESI) and the Sloan Digital Sky Survey (SDSS). We find no significant tension between DESI and SDSS. △ Less

Submitted 24 February, 2026; v1 submitted 22 July, 2024; originally announced July 2024.

Comments: Updated to be in line with the journal

Resonant or asymmetric: The status of sub-GeV dark matter

Authors: Sowmiya Balan, Csaba Balázs, Torsten Bringmann, Christopher Cappiello, Riccardo Catena, Timon Emken, Tomás E. Gonzalo, Taylor R. Gray, Will Handley, Quan Huynh, Felix Kahlhoefer, Aaron C. Vincent

Abstract: Sub-GeV dark matter (DM) particles produced via thermal freeze-out evade many of the strong constraints on heavier DM candidates but at the same time face a multitude of new constraints from laboratory experiments, astrophysical observations and cosmological data. In this work we combine all of these constraints in order to perform frequentist and Bayesian global analyses of fermionic and scalar s… ▽ More Sub-GeV dark matter (DM) particles produced via thermal freeze-out evade many of the strong constraints on heavier DM candidates but at the same time face a multitude of new constraints from laboratory experiments, astrophysical observations and cosmological data. In this work we combine all of these constraints in order to perform frequentist and Bayesian global analyses of fermionic and scalar sub-GeV DM coupled to a dark photon with kinetic mixing. For fermionic DM, we find viable parameter regions close to the dark photon resonance, which expand significantly when including a particle-antiparticle asymmetry. For scalar DM, the velocity-dependent annihilation cross section evades the strongest constraints even in the symmetric case. Using Bayesian model comparison, we show that both asymmetric fermionic DM and symmetric scalar DM are preferred over symmetric fermionic DM due to the reduced fine-tuning penalty. Finally, we explore the discovery prospects of near-future experiments both in the full parameter space and for specific benchmark points. We find that the most commonly used benchmark scenarios are already in tension with existing constraints and propose a new benchmark point that can be targeted with future searches. △ Less

Submitted 26 May, 2025; v1 submitted 27 May, 2024; originally announced May 2024.

Comments: Published version with updated figures and citations

Report number: TTP24-015, P3H-24-033

Journal ref: JCAP01(2025)053

Costless correction of chain based nested sampling parameter estimation in gravitational wave data and beyond

Authors: Metha Prathaban, Will Handley

Abstract: Nested sampling parameter estimation differs from evidence estimation, in that it incurs an additional source of uncertainty. This uncertainty affects estimates of parameter means and credible intervals in gravitational wave analyses and beyond, and yet, it is typically not accounted for in standard uncertainty estimation methods. In this paper, we present two novel methods to quantify this uncert… ▽ More Nested sampling parameter estimation differs from evidence estimation, in that it incurs an additional source of uncertainty. This uncertainty affects estimates of parameter means and credible intervals in gravitational wave analyses and beyond, and yet, it is typically not accounted for in standard uncertainty estimation methods. In this paper, we present two novel methods to quantify this uncertainty more accurately for any chain based nested sampler, using the additional likelihood calls made at runtime in producing independent samples. Using injected signals of black hole binary coalescences as an example, we first show concretely that the usual uncertainty estimation method is insufficient to capture the true error bar on parameter estimates. We then demonstrate how the extra points in the chains of chain based samplers may be carefully utilised to estimate this uncertainty correctly, and provide a way to check the accuracy of the resulting error bars. Finally, we discuss how this uncertainty affects $p$-$p$ plots and coverage assessments. △ Less

Submitted 4 November, 2025; v1 submitted 25 April, 2024; originally announced April 2024.

Comments: Version published in MNRAS

Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH

Authors: Anchal Saxena, P. Daniel Meerburg, Christoph Weniger, Eloy de Lera Acedo, Will Handley

Abstract: The redshifted 21-cm signal from the Cosmic Dawn and Epoch of Reionization carries invaluable information about the cosmology and astrophysics of the early Universe. Analyzing data from a sky-averaged 21-cm signal experiment requires navigating through an intricate parameter space addressing various factors such as foregrounds, beam uncertainties, ionospheric distortions, and receiver noise for th… ▽ More The redshifted 21-cm signal from the Cosmic Dawn and Epoch of Reionization carries invaluable information about the cosmology and astrophysics of the early Universe. Analyzing data from a sky-averaged 21-cm signal experiment requires navigating through an intricate parameter space addressing various factors such as foregrounds, beam uncertainties, ionospheric distortions, and receiver noise for the search of the 21-cm signal. The traditional likelihood-based sampling methods for modeling these effects could become computationally demanding for such complex models, which makes it infeasible to include physically motivated 21-cm signal models in the analysis. Moreover, the inference is driven by the assumed functional form of the likelihood. We demonstrate how Simulation-Based Inference through Truncated Marginal Neural Ratio Estimation (TMNRE) can naturally handle these issues at a reduced computational cost. We estimate the posterior distribution on our model parameters with TMNRE for simulated mock observations, incorporating beam-weighted foregrounds, physically motivated 21-cm signal, and radiometric noise. We find that maximizing information content by analyzing data from multiple time slices and antennas significantly improves the parameter constraints and enhances the exploration of the cosmological signal. We discuss the application of TMNRE for the current configuration of the REACH experiment and demonstrate its potential for exploring new avenues. △ Less

Submitted 12 June, 2025; v1 submitted 21 March, 2024; originally announced March 2024.

Comments: 14 pages, 10 figures, Published in RAS Techniques and Instruments

Journal ref: RAS Techniques and Instruments, Volume 3, Issue 1, January 2024, Pages 724-736

A foundation model for atomistic materials chemistry

Authors: Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Filippo Bigi, Samuel M. Blau, Vlad Cărare, Michele Ceriotti, Sanggyu Chong, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Fabio Falcioni , et al. (63 additional authors not shown)

Abstract: Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time… ▽ More Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model - and its qualitative and at times quantitative accuracy - on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields. △ Less

Submitted 4 September, 2025; v1 submitted 29 December, 2023; originally announced January 2024.

Comments: 154 pages, 83 figures, 80MB PDF