arXiv subjects

Harry Bevins

Publications and source records attributed to Harry Bevins.

6 recordsLinked to original sources

arXiv2026

Synchrotron and free-free mapping with simulated REACH observations between 50-170 MHz

Global 21cm experiments aim to detect the hydrogen 21cm signal by separating it from foreground emission that can be orders of magnitude brighter than the signal. REACH (the Radio Experiment for the Analysis of Cosmic Hydrogen) forward-models the sky by jointly fitting signal and foreground spectral parameters to an existing sky map. The fitted parameters yield spectrally constrained, absolutely calibrated maps of the radio sky across the full 50-170 MHz observing band, among the lowest continuous frequencies yet mapped. We assess REACH's ability to fit the 21cm signal and recover accurate foreground maps, using physically motivated foreground models of increasing complexity (starting from a pure synchrotron power law model, then introducing variable amplitudes, curvature, and a free-free component). We evaluate these models against simulated REACH observations of correspondingly complex foregrounds, based on the Global Sky Model and the Python Sky Model. To recover the 21cm signal, more complex datasets require correspondingly complex models, but this introduces degeneracies which limit accurate recovery of foreground parameters. Fitting a foreground with independent synchrotron and free-free emission enables component-separated sky mapping, which has applications beyond radio cosmology; synchrotron is well-recovered across the sky, but free-free recovery is limited. REACH is therefore capable of probing Galactic physics at uniquely low frequencies, alongside its primary goal of detecting the 21cm signal.

astro-ph.CO↗

arXiv2026

Implications of SARAS3 data for Coulomb-like interacting dark matter

The 21-cm signal from cosmic dawn is a potentially sensitive probe of interactions between dark matter (DM) and baryons. We investigate the implications of the SARAS3 non-detection in the 55.5-84.4 MHz band for Coulomb-like interacting DM (IDM). In contrast to earlier constraint analyses that focused primarily on baryon cooling, we model the interaction self-consistently by including both excess cooling of the gas and the suppression of structure formation, which delays the onset of star formation and hence suppresses the Ly$\alpha$, X-ray, and ionizing backgrounds at early times. We perform a joint Bayesian fit of a global 21-cm signal model and a flexible foreground model to the SARAS3 antenna temperature, and find that the signal parameters remain weakly constrained after marginalizing over the foregrounds. The null result is nonetheless informative: the data disfavour deep absorption features within the observed band, with the strongest bound at $z = 23.6$ ($\nu \approx 57.7$ MHz), where $T_{21} \gtrsim -277.6$ mK at $3\sigma$. Comparing the IDM and standard cold dark matter scenarios, we find no statistically significant preference for IDM (Bayes factor $B \approx 1.7$). While we do not constrain the strength of baryon-DM interactions, the SARAS3 non-detection places a meaningful upper bound on the amplitude of the global 21-cm signal in this class of models.

astro-ph.CO↗

arXiv2026

Circuit Modeling for In Situ 21 cm Radiometer Calibration

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

astro-ph.IM↗

arXiv2024

Accelerated nested sampling with posterior repartitioning and $\beta$-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 $\beta$-flow, a novel type of conditional normalizing flow designed to better learn deep tail probabilities. $\beta$-flows are trained on the entire nested sampling run and conditioned on an inverse temperature $\beta$. 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 $\beta$-flows over standard normalizing flows for posterior repartitioning. Notably, $\beta$-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↗

arXiv2023

Piecewise Normalizing Flows

Normalizing flows are an established approach for modelling complex probability densities through invertible transformations from a base distribution. However, the accuracy with which the target distribution can be captured by the normalizing flow is strongly influenced by the topology of the base distribution. A mismatch between the topology of the target and the base can result in a poor performance, as is typically the case for multi-modal problems. A number of different works have attempted to modify the topology of the base distribution to better match the target, either through the use of Gaussian Mixture Models (Izmailov et al., 2020; Ardizzone et al., 2020; Hagemann & Neumayer, 2021) or learned accept/reject sampling (Stimper et al., 2022). We introduce piecewise normalizing flows which divide the target distribution into clusters, with topologies that better match the standard normal base distribution, and train a series of flows to model complex multi-modal targets. We demonstrate the performance of the piecewise flows using some standard benchmarks and compare the accuracy of the flows to the approach taken in Stimper et al. (2022) for modelling multi-modal distributions. We find that our approach consistently outperforms the approach in Stimper et al. (2022) with a higher emulation accuracy on the standard benchmarks.

stat.ML↗

arXiv2022

Marginal Bayesian Statistics Using Masked Autoregressive Flows and Kernel Density Estimators with Examples in Cosmology

Cosmological experiments often employ Bayesian workflows to derive constraints on cosmological and astrophysical parameters from their data. It has been shown that these constraints can be combined across different probes such as Planck and the Dark Energy Survey and that this can be a valuable exercise to improve our understanding of the universe and quantify tension between multiple experiments. However, these experiments are typically plagued by differing systematics, instrumental effects and contaminating signals, which we collectively refer to as `nuisance' components, that have to be modelled alongside target signals of interest. This leads to high dimensional parameter spaces, especially when combining data sets, with > 20 dimensions of which only around 5 correspond to key physical quantities. We present a means by which to combine constraints from different data sets in a computationally efficient manner by generating rapid, reusable and reliable marginal probability density estimators, giving us access to nuisance-free likelihoods. This is possible through the unique combination of nested sampling, which gives us access to Bayesian evidences, and the marginal Bayesian statistics code MARGARINE. Our method is lossless in the signal parameters, resulting in the same posterior distributions as would be found from a full nested sampling run over all nuisance parameters, and typically quicker than evaluating full likelihoods. We demonstrate our approach by applying it to the combination of posteriors from the Dark Energy Survey and Planck.

astro-ph.CO↗