arXiv subjects

James Alvey

Publications and source records attributed to James Alvey.

At least 19 recordsLinked to original sources

arXiv2026

Ab Initio Real-Time Gravitational-Wave Parameter Estimation

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

gr-qc↗

arXiv2026

Impact of non-Gaussian likelihood on cosmological constraints from the thermal Sunyaev--Zel'dovich power spectrum: a simulation-based inference analysis

The thermal Sunyaev--Zel'dovich (tSZ) power spectrum is a sensitive probe of cosmology and cluster astrophysics, but its statistics are non-Gaussian because the signal receives a significant contribution from rare, massive, low-redshift galaxy clusters. As a result, a Gaussian likelihood fails to describe the statistics of its power spectrum on large scales. We use simulation-based inference (SBI) to test the accuracy of the standard Gaussian power-spectrum likelihood for a \textit{Planck}-like tSZ analysis. Using halo-based simulations of full-sky Compton-$y$ maps, we train neural posterior and likelihood estimators and compare the resulting constraints with those from a Gaussian likelihood assumption. Using only multipoles $\ell < 1000$, we find that the Gaussian likelihood assumption gives unbiased cosmological constraints, while the SBI-based inference shows a mild broadening of the posterior distributions for the amplitudes of residual foregrounds. This suggests that the Gaussian likelihood assumption is sufficiently accurate for cosmological inference for a \textit{Planck}-like tSZ analysis, while SBI provides a useful validation tool to model non-Gaussian likelihoods beyond analytic approximations.

astro-ph.CO↗

arXiv2026

Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference

Over the last five years, multiple Pulsar Timing Array (PTA) collaborations have reported mounting evidence for a gravitational-wave background (GWB) at nanohertz frequencies. Measuring anisotropies in the sky distribution of the GWB power is one of the most promising ways to identify and characterize its source. These anisotropies are expected to manifest as deviations from the Hellings-Downs (HD) correlations between the timing residuals of different pulsars. Current search strategies include Bayesian methods, which model anisotropies in the timing residuals likelihood, and faster frequentist approaches, which construct correlation estimators from timing residuals and use these to test the isotropic assumption. However, frequentist methods rely on the assumption that correlation estimators are Gaussian-distributed, an assumption that is not justified and that -- as we will show -- severely limits detection sensitivity. In this work, we present a Simulation-Based Inference (SBI) framework that replaces the analytic Gaussian likelihood used in frequentist searches with a neural network classifier trained on synthetic data. This approach captures the non-Gaussian structure of the data and significantly improves performance. Specifically, we find that the probability of $3\sigma$ detection increases by approximately 90% for single-hotspot scenarios and by 200% for double-hotspot scenarios compared to standard frequentist methods.

astro-ph.CO↗

arXiv2025

Dynamic SBI: Round-free Sequential Simulation-Based Inference with Adaptive Datasets

Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational power of deep neural networks, SBI can extract the most informative simulation features for the parameters of interest. Sequential SBI methods extend this approach by iteratively steering the simulation process towards the most relevant regions of parameter space. This is typically implemented through an algorithmic structure, in which simulation and network training alternate over multiple rounds. This strategy is particularly well suited for high-precision inference in high-dimensional settings, which are commonplace in physics applications with growing data volumes and increasing model fidelity. Here, we introduce dynamic SBI, which implements the core ideas of sequential methods in a round-free, asynchronous, and highly parallelisable manner. At its core is an adaptive dataset that is iteratively transformed during inference to resemble the target observation. Simulation and training proceed in parallel: trained networks are used both to filter out simulations incompatible with the data and to propose new, more promising ones. Compared to round-based sequential methods, this asynchronous structure can significantly reduce simulation costs and training overhead. We demonstrate that dynamic SBI achieves significant improvements in simulation and training efficiency while maintaining inference performance. We further validate our framework on two challenging astrophysical inference tasks: characterising the stochastic gravitational wave background and analysing strong gravitational lensing systems. Overall, this work presents a flexible and efficient new paradigm for sequential SBI.

astro-ph.IM↗

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

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

Simulation-based inference with deep ensembles: Evaluating calibration uncertainty and detecting model misspecification

Simulation-Based Inference (SBI) offers a principled and flexible framework for conducting Bayesian inference in any situation where forward simulations are feasible. However, validating the accuracy and reliability of the inferred posteriors remains a persistent challenge. In this work, we point out a simple diagnostic approach rooted in ensemble learning methods to assess the internal consistency of SBI outputs that does not require access to the true posterior. By training multiple neural estimators under identical conditions and evaluating their pairwise Kullback-Leibler (KL) divergences, we define a consistency criterion that quantifies agreement across the ensemble. We highlight two core use cases for this framework: a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g., too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.

astro-ph.IM↗

arXiv2025

Sequential simulation-based inference for extreme mass ratio inspirals

Extreme mass-ratio inspirals pose a difficult challenge in terms of both search and parameter estimation for upcoming space-based gravitational-wave detectors such as LISA. Their signals are long and of complex morphology, meaning they carry a large amount of information about their source, but their waveforms are expensive to compute and they occupy a vast and multi-modal parameter space. We explore how sequential simulation-based inference methods, specifically truncated marginal neural ratio estimation, could offer solutions to some of the challenges surrounding extreme-mass-ratio inspiral data analysis. We show that this method can efficiently narrow down the volume of the complex 11-dimensional search parameter space by a factor of $10^6-10^7$ and provide 1-dimensional marginal proposal distributions for non-spinning extreme-mass-ratio inspirals. We discuss the current limitations of this approach and place it in the broader context of a global strategy for future space-based gravitational-wave data analysis.

gr-qc↗

arXiv2024

Tests for model misspecification in simulation-based inference: from local distortions to global model checks

Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few years, there has been a rapid rise in the use of simulation-based inference (SBI) techniques for Bayesian parameter estimation, applied to increasingly complex forward models. To move towards fully simulation-based analysis pipelines, however, there is an urgent need for a comprehensive simulation-based framework for model misspecification analysis. In this work, we provide a solid and flexible foundation for a wide range of model discrepancy analysis tasks, using distortion-driven model misspecification tests. From a theoretical perspective, we introduce the statistical framework built around performing many hypothesis tests for distortions of the simulation model. We also make explicit analytic connections to classical techniques: anomaly detection, model validation, and goodness-of-fit residual analysis. Furthermore, we introduce an efficient self-calibrating training algorithm that is useful for practitioners. We demonstrate the performance of the framework in multiple scenarios, making the connection to classical results where they are valid. Finally, we show how to conduct such a distortion-driven model misspecification test for real gravitational wave data, specifically on the event GW150914.

astro-ph.IM↗

arXiv2024

Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis

Variations in the instrumental noise of the Laser Interferometer Space Antenna (LISA) over time are expected as a result of e.g. scheduled satellite operations or unscheduled glitches. We demonstrate that these fluctuations can be leveraged to improve the sensitivity to stochastic gravitational wave backgrounds (SGWBs) compared to the stationary noise scenario. This requires optimal use of data segments with downward noise fluctuations, and thus a data analysis pipeline capable of analysing and combining shorter time segments of mission data. We propose that simulation based inference is well suited for this challenge. In an approximate, but state-of-the-art, modeling setup, we show by comparison with Fisher Information Matrix estimates that the optimal information gain can be achieved in practice.

gr-qc↗

arXiv2024

Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way

Stellar streams are potentially a very sensitive observational probe of galactic astrophysics, as well as the dark matter population in the Milky Way. On the other hand, performing a detailed, high-fidelity statistical analysis of these objects is challenging for a number of key reasons. Firstly, the modelling of streams across their (potentially billions of years old) dynamical age is complex and computationally costly. Secondly, their detection and classification in large surveys such as Gaia renders a robust statistical description regarding e.g., the stellar membership probabilities, challenging. As a result, the majority of current analyses must resort to simplified models that use only subsets or summaries of the high quality data. In this work, we develop a new analysis framework that takes advantage of advances in simulation-based inference techniques to perform complete analysis on complex stream models. To facilitate this, we develop a new, modular dynamical modelling code sstrax for stellar streams that is highly accelerated using jax. We test our analysis pipeline on a mock observation that resembles the GD1 stream, and demonstrate that we can perform robust inference on all relevant parts of the stream model simultaneously. Finally, we present some outlook as to how this approach can be developed further to perform more complete and accurate statistical analyses of current and future data.

astro-ph.GA↗

arXiv2024

Peregrine: Sequential simulation-based inference for gravitational wave signals

The current and upcoming generations of gravitational wave experiments represent an exciting step forward in terms of detector sensitivity and performance. For example, key upgrades at the LIGO, Virgo and KAGRA facilities will see the next observing run (O4) probe a spatial volume around four times larger than the previous run (O3), and design implementations for e.g. the Einstein Telescope, Cosmic Explorer and LISA experiments are taking shape to explore a wider frequency range and probe cosmic distances. In this context, however, a number of very real data analysis problems face the gravitational wave community. For example, it will be crucial to develop tools and strategies to analyse (amongst other scenarios) signals that arrive coincidentally in detectors, longer signals that are in the presence of non-stationary noise or other shorter transients, as well as noisy, potentially correlated, coherent stochastic backgrounds. With these challenges in mind, we develop peregrine, a new sequential simulation-based inference approach designed to study broad classes of gravitational wave signal. In this work, we describe the method and implementation, before demonstrating its accuracy and robustness through direct comparison with established likelihood-based methods. Specifically, we show that we are able to fully reconstruct the posterior distributions for every parameter of a spinning, precessing compact binary coalescence using one of the most physically detailed and computationally expensive waveform approximants (SEOBNRv4PHM). Crucially, we are able to do this using only 2% of the waveform evaluations that are required in e.g. nested sampling approaches. Finally, we provide some outlook as to how this level of simulation efficiency and flexibility in the statistical analysis could allow peregrine to tackle these current and future gravitational wave data analysis problems.

gr-qc↗

arXiv2024

Simulation-based inference for stochastic gravitational wave background data analysis

The next generation of space- and ground-based facilities promise to reveal an entirely new picture of the gravitational wave sky: thousands of galactic and extragalactic binary signals, as well as stochastic gravitational wave backgrounds (SGWBs) of unresolved astrophysical and possibly cosmological signals. These will need to be disentangled to achieve the scientific goals of experiments such as LISA, Einstein Telescope, or Cosmic Explorer. We focus on one particular aspect of this challenge: reconstructing an SGWB from (mock) LISA data. We demonstrate that simulation-based inference (SBI) - specifically truncated marginal neural ratio estimation (TMNRE) - is a promising avenue to overcome some of the technical difficulties and compromises necessary when applying more traditional methods such as Monte Carlo Markov Chains (MCMC). To highlight this, we show that we can reproduce results from traditional methods both for a template-based and agnostic search for an SGWB. Moreover, as a demonstration of the rich potential of SBI, we consider the injection of a population of low signal-to-noise ratio supermassive black hole transient signals into the data. TMNRE can implicitly marginalize over this complicated parameter space, enabling us to directly and accurately reconstruct the stochastic (and instrumental noise) contributions. We publicly release our TMNRE implementation in the form of the code saqqara.

gr-qc↗

arXiv2024

Dwarf galaxies imply dark matter is heavier than $\mathbf{2.2 \times 10^{-21}} \, \mathbf{eV}$

It is widely established that a lower bound on the dark matter particle mass, $m$, can be obtained by demanding that the de Broglie wavelength in a given galaxy must be smaller than the virial radius of the galaxy, leading to $m\gtrsim 10^{-22}\text{ eV}$ when applied to typical dwarf galaxies. This lower limit has never been derived precisely or rigorously. We use stellar kinematical data for the Milky Way satellite galaxy Leo II to self-consistently reconstruct a statistical ensemble of dark matter wavefunctions and corresponding density profiles. By comparison to a data-driven, model-independent reconstruction, and using a variant of the maximum mean discrepancy as a statistical measure, we determine that a self-consistent description of dark matter in the local Universe requires $m>2.2 \times 10^{-21}\,\mathrm{eV}\;\mathrm{(CL>95\%)}$. This lower limit is free of any assumptions pertaining to cosmology, microphysics (including spin), or dynamics of dark matter, and only assumes that it is predominantly composed of a single bosonic particle species.

astro-ph.CO↗

arXiv2024

Scaling-laws for Large Time-series Models

Scaling laws for large language models (LLMs) have provided useful guidance in training ever larger models for predictable performance gains. Time series forecasting shares a similar sequential structure to language, and is amenable to large-scale transformer architectures. Here we show that foundational decoder-only time series transformer models exhibit analogous scaling-behavior to LLMs, with architectural details (aspect ratio and number of heads) having a minimal effect over broad ranges. We assemble a large corpus of heterogenous time series data on which to train, and establish for the first time power-law scaling with parameter count, dataset size, and training compute, spanning five orders of magnitude.

cs.LG↗

arXiv2023

Scalable inference with Autoregressive Neural Ratio Estimation

In recent years, there has been a remarkable development of simulation-based inference (SBI) algorithms, and they have now been applied across a wide range of astrophysical and cosmological analyses. There are a number of key advantages to these methods, centred around the ability to perform scalable statistical inference without an explicit likelihood. In this work, we propose two technical building blocks to a specific sequential SBI algorithm, truncated marginal neural ratio estimation (TMNRE). In particular, first we develop autoregressive ratio estimation with the aim to robustly estimate correlated high-dimensional posteriors. Secondly, we propose a slice-based nested sampling algorithm to efficiently draw both posterior samples and constrained prior samples from ratio estimators, the latter being instrumental for sequential inference. To validate our implementation, we carry out inference tasks on three concrete examples: a toy model of a multi-dimensional Gaussian, the analysis of a stellar stream mock observation, and finally, a proof-of-concept application to substructure searches in strong gravitational lensing. In addition, we publicly release the code for both the autoregressive ratio estimator and the slice sampler.

astro-ph.IM↗

arXiv2023

What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals

The gravitational wave sky is starting to become very crowded, with the fourth science run (O4) at LIGO expected to detect $\mathcal{O}(100)$ compact object coalescence signals. Data analysis issues start to arise as we look further forwards, however. In particular, as the event rate increases in e.g. next generation detectors, it will become increasingly likely that signals arrive in the detector coincidentally, eventually becoming the dominant source class. It is known that current analysis pipelines will struggle to deal with this scenario, predominantly due to the scaling of traditional methods such as Monte Carlo Markov Chains and nested sampling, where the time difference between analysing a single signal and multiple can be as significant as days to months. In this work, we argue that sequential simulation-based inference methods can solve this problem by breaking the scaling behaviour. Specifically, we apply an algorithm known as (truncated marginal) neural ratio estimation (TMNRE), implemented in the code peregrine and based on swyft. To demonstrate its applicability, we consider three case studies comprising two overlapping, spinning, and precessing binary black hole systems with merger times separated by 0.05 s, 0.2 s, and 0.5 s. We show for the first time that we can recover, with full precision (as quantified by a comparison to the analysis of each signal independently), the posterior distributions of all 30 model parameters in a full joint analysis. Crucially, we achieve this with only $\sim 15\%$ of the waveform evaluations that would be needed to analyse even a single signal with traditional methods.

gr-qc↗

arXiv2023

No room to hide: implications of cosmic-ray upscattering for GeV-scale dark matter

The irreducible upscattering of cold dark matter by cosmic rays opens up the intriguing possibility of detecting even light dark matter in conventional direct detection experiments or underground neutrino detectors. The mechanism also significantly enhances sensitivity to models with very large nuclear scattering rates, where the atmosphere and rock overburden efficiently stop standard non-relativistic dark matter particles before they could reach the detector. In this article, we demonstrate that cosmic-ray upscattering essentially closes the window for strongly interacting dark matter in the (sub-)GeV mass range. Arriving at this conclusion crucially requires a detailed treatment of both nuclear form factors and inelastic dark matter-nucleus scattering, as well as including the full momentum-transfer dependence of scattering amplitudes. We illustrate the latter point by considering three generic situations where such a momentum-dependence is particularly relevant, namely for interactions dominated by the exchange of light vector or scalar mediators, respectively, and for dark matter particles of finite size. As a final concrete example, we apply our analysis to a putative hexaquark state, which has been suggested as a viable baryonic dark matter candidate. Once again, we find that the updated constraints derived in this work close a significant part of otherwise unconstrained parameter space.

hep-ph↗