arXiv subjects

Will Handley

Publications and source records attributed to Will Handley.

At least 37 records · Page 2Linked to original sources

arXiv2025

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

Bayesian inference is central to modern cosmology. While parameter estimation is achievable with unnormalised posteriors traditionally obtained via MCMC methods, comprehensive model comparison and tension quantification require Bayesian evidences and normalised posteriors, which remain computationally prohibitive for many researchers. To address this, we present $\texttt{unimpeded}$, a publicly available Python library and data repository providing DiRAC-funded (DP192 and 264) pre-computed nested sampling and MCMC chains with their normalised posterior samples, computed using $\texttt{Cobaya}$ and the Boltzmann solver $\texttt{CAMB}$. $\texttt{unimpeded}$ delivers systematic analysis across a grid of eight cosmological models (including $Λ$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.

astro-ph.IM↗

arXiv2025

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

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.

astro-ph.IM↗

arXiv2025

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

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.

astro-ph.IM↗

arXiv2025

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

We demonstrate a GPU-accelerated nested sampling framework for efficient high-dimensional Bayesian inference in cosmology. Using JAX-based neural emulators and likelihoods for cosmic microwave background and cosmic shear analyses, our approach provides parameter constraints and direct calculation of Bayesian evidence. In the 39-dimensional $Λ$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.

astro-ph.CO↗

arXiv2025

Parallel Nested Slice Sampling for Gravitational Wave Parameter Estimation

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

astro-ph.IM↗

arXiv2025

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

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 $[-0.076,-0.039, -0.024, -0.016, -0.012, \dots]$.

astro-ph.CO↗

arXiv2025

A foundation model for atomistic materials chemistry

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.

physics.chem-ph↗

arXiv2025

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

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.

gr-qc↗

arXiv2025

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

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]

astro-ph.CO↗

arXiv2025

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

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.

astro-ph.CO↗

arXiv2025

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

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.

hep-ph↗

arXiv2025

Resonances in reflective Hamiltonian Monte Carlo

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.

stat.ML↗

arXiv2025

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

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.

astro-ph.IM↗

arXiv2025

Receiver design for the REACH global 21-cm signal experiment

We detail the the REACH radiometric system designed to enable measurements of the 21-cm neutral hydrogen line. Included is the radiometer architecture and end-to-end system simulations as well as a discussion of the challenges intrinsic to highly-calibratable system development. Following this, we share laboratory results based on the calculation of noise wave parameters utilising an over-constrained least squares approach demonstrating a calibration RMSE of 80 mK for five hours of integration on a custom-made source with comparable impedance to that of the antenna used in the field. This paper therefore documents the state of the calibrator and data analysis in December 2022 in Cambridge before shipping to South Africa.

astro-ph.IM↗

arXiv2025

Cosmological Parameter Estimation with Sequential Linear Simulation-based Inference

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.

astro-ph.IM↗

arXiv2025

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

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.

astro-ph.IM↗

arXiv2024

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

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.

hep-ph↗

arXiv2024

Towards a Field Based Bayesian Evidence Inference from Nested Sampling Data

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.

physics.comp-ph↗