arXiv subjects

Emma Tolley

Publications and source records attributed to Emma Tolley.

At least 19 recordsLinked to original sources

arXiv2026

Multi-branch classification of diffuse cluster radio emission from the LOFAR two-metre sky survey

Context. Galaxy clusters sometimes host synchrotron radiation on scales of approximately 100 kpc to approximately 1 Mpc, with a surface brightness only a few times the image noise. This diffuse cluster radio emission is a sensitive probe of magnetic fields and intracluster medium dynamics, but disentangling the underlying physical processes requires statistically large samples spanning a wide range of cluster masses, dynamical states, and redshifts, together with a sufficient sensitivity to low-surface-brightness emission. Aims. We explore two techniques for improving the detection of diffuse emission in galaxy cluster images, relative to a baseline classifier: the scattering transform (ST) and squeeze-excitation (SE) attention. Methods. We integrated an ST encoder into a dual-branch classifier (DualSSN) and a scattering network (ScatterNet). We incorporated SE attention into the DualSSN and dual-branch convolutional neural network (DualCSN). These classifiers were then benchmarked against a simple convolutional neural network (CNN), across ten image pre-processing configurations and three cropping strategies. Performance was evaluated on small labelled datasets from the second data release of the LOFAR Two-metre Sky Survey overlapping with the Second Planck catalogue of Sunyaev-Zel'dovich sources (LoTSS-DR2/PSZ2). Results. Alongside the multi-branch approach with SE and ST, cropping the image to a fixed number of telescope beams and uv tapering (smoothing to a coarser angular resolution) improve classification performance, while tacking multiple pre-processed versions of an image does not. Conclusions. Scattering-transform-based multi-branch architectures with beam-normalised cropping are a promising direction for diffuse emission classification in the SKA era.

astro-ph.CO↗

arXiv2026

Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks

Generative machine learning models have recently emerged as powerful tools for producing cosmological simulations. However, many existing emulators do not explicitly enforce the underlying physical dynamics governing cosmological evolution, often leading to artifacts and poor adherence to the evolution equations. In this work, we present a physics-informed generative U-Net framework for fuzzy dark matter (FDM) that addresses two complementary tasks: (i) the evolution of cosmological fields from initial conditions to an arbitrary cosmological scale factor and (ii) the super-resolution of FDM simulations at a specified cosmological scale factor. Our model incorporates a physics-informed loss function that explicitly enforces consistency with the underlying Schr\"odinger-Poisson (SP) dynamics during training. For the evolution task, we find that the inclusion of this physics-based loss significantly improves the quality of the predicted simulations, even when only a small amount of training data is available. Using only 20% of the training data, the model accurately reproduces the target simulations in a 1 $h^{-1}$ Mpc box. Furthermore, the framework generalizes effectively across previously unseen realizations of the initial conditions. For the super-resolution task, we present, for the first time, a generative super-resolution model trained on FDM simulations obtained by solving the full SP equations, considering both single and multiple realizations of the initial conditions and analyzing the role of the physics-informed loss in each case. Our approach enables modern generative modeling of cosmological simulations while maintaining physical consistency and substantially reducing generative artifacts.

astro-ph.CO↗

arXiv2026

Foreground Characterization and Mitigation in the Observations of the CD/EoR with the SKA

The Square Kilometre Array (SKA), with its unprecedented sensitivity, frequency coverage, and large collecting area, is poised to revolutionize our understanding of the Cosmic Dawn (CD) and Epoch of Reionization (EoR) epochs marking the formation of the first luminous sources and the subsequent reionization of the intergalactic medium (IGM). However, detecting the faint redshifted 21-cm signal from neutral hydrogen remains one of the foremost challenges in observational cosmology, as it is buried beneath bright foregrounds from Galactic synchrotron radiation, free-free emission, and extragalactic point sources that are 4-5 orders of magnitude stronger than the cosmological signal. In this chapter, we highlight the key components and characteristics of these foregrounds and review ongoing efforts to model, characterize, and mitigate them. We emphasize how the SKA-Low AA* configuration, through its optimized array design, wide field of view, and improved calibration accuracy, enhances our capacity to suppress foreground contamination and recover the cosmological signal. The SKA Observatory Foreground Challenge plays a pivotal role in this effort by bringing together the global EoR/CD community to develop, compare, and validate foreground removal pipelines using realistic simulated datasets. Building on the experience of existing pathfinders such as LOFAR, MWA, and HERA, these collaborative initiatives are helping refine statistical and machine learning-based approaches for signal recovery. Together, these advancements are laying the groundwork for the SKA to probe the thermal and ionization history of the early Universe with unprecedented precision.

astro-ph.CO↗

arXiv2026

Probing the Fundamental Nature of Particle Dark Matter

Understanding the fundamental nature of dark matter (DM) is one of the most significant scientific challenges of our time. A compelling hypothesis is that DM consists of a new, yet-to-be-discovered particle. Among the leading candidates are weakly interacting massive particles (WIMPs) and axion-like particles (ALPs), both of which can be investigated using observations with the SKA telescopes. In this chapter, we review the search for particle DM through radio observations, summarizing the current state-of-the-art and presenting forecasts for the SKA-Low and SKA-Mid telescopes in the AA4 baseline design. Radio searches for WIMPs focus on detecting synchrotron radiation originating from the products of DM annihilation using continuum observations. Competitive constraints on sub-TeV WIMPs have already been derived using SKA precursors looking at dwarf galaxies, galaxy clusters, and the Large Magellanic Cloud. We discuss how the superior continuum sensitivity of the SKA telescopes will allow us to progressively close in on the WIMP parameter space. The ALP signal arises from its decay or conversion into photon(s), which typically consists of a nearly monochromatic signature, and from rotation of polarization angles of photons interacting with ALPs. We demonstrate how the spectral resolution, line sensitivity, and polarimetry of the SKA AA4 telescopes can be leveraged to constrain the ALP-photon coupling.

astro-ph.CO↗

arXiv2026

Forecasting the occupancy of satellite megaconstellations in SKA observations

The Square Kilometre Array (SKA) is expected to start science operations in 2030 and by that time there could be up to 10$^5$ artificial satellites in Earth's orbit, comprising an increase of an order of magnitude compared to 2024. Most of these new satellites will belong to satellite megaconstellations aimed at providing communication services all over Earth. These satellites create radio frequency interference (RFI) that can impact the observations of modern radio telescopes. In this Letter, we forecast the amount of observing time for which the SKA interferometers will be exposed to satellites, risking RFI contamination. We employed an analytical model and considered two cases of exposure to satellites; (1) satellites that only lie in the main beam and (2) satellites that lie in the main beam or the first sidelobe. We show that for SKA-Low, the exposure is high, with satellites in the beam for 30% of the observation time across half of the frequency range, rising up to 100% below 100 MHz. For SKA-Mid, high frequencies are mostly spared, but observations below 1 GHz could also end up seeing satellites for at least 30% of the time. We conclude that satellites will be unavoidable during SKA observing conditions, risking a strong impact on the RFI environment. This will necessitate a concerted effort to obtain accurate measurements of satellite RFI and to improve our understanding of the impact on various science cases. Finally, new mitigation techniques that are less data-destructive than simple flagging must be introduced.

astro-ph.IM↗

arXiv2026

A targeted machine learning approach for detecting diffuse radio emission with Astronomaly: Protege

Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-cluster medium. However, their low surface brightness, as well as contamination from compact sources and imaging artefacts, makes their detection challenging. The sheer volume of data from instruments such as the Square Kilometre Array will render traditional manual-inspection based detection methods infeasible. This paper introduces a novel machine learning approach that uses active learning to rapidly identify diffuse emission candidates from a small, optimally-selected subset of data. We apply the self-supervised deep learning algorithm Bootstrap Your Own Latent to extract features from source cutouts in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We then pass these features through the Astronomaly: Protege anomaly detection framework to identify the final candidates. Using a human-labelled set, we evaluate our pipeline on high-resolution (~7''), convolved (15''), and combined-feature MGCLS datasets. Interestingly, the high-resolution features identify diffuse sources more efficiently than the convolved resolution, which are in turn outperformed by the combined features. Of the top 100 sources ranked by Protege, 99% exhibit diffuse characteristics, with 55% confirmed as cluster-related emission. Our work shows that Protege can identify diffuse emission with minimal human labelling effort, offering a powerful, scalable tool capable of detecting both known and novel diffuse radio sources.

astro-ph.IM↗

arXiv2026

Radio-Interferometric Image Reconstruction with Denoising Diffusion Restoration Models

Reconstructing images of the radio sky from incomplete Fourier information is a key challenge in radio astronomy. In this work, we present a method for radio interferometric image reconstruction using a data-driven prior for the radio sky based on denoising diffusion probabilistic models (DDPMs). We train a DDPM on radio galaxy observations from the VLA FIRST survey, then create simulated VLBA, EHT, and ALMA observations of radio galaxies. We use an unsupervised posterior sampling method called Denoising Diffusion Restoration Models (DDRM) to reconstruct the corresponding images using our DDPM as a prior. Our approach naturally incorporates the PSF of the instrument. We are able to reconstruct images with very high fidelity and demonstrate a marked improvement over CLEAN and MS CLEAN. While DDRM naturally produces multiple samples, these are not calibrated and do not constitute reliable uncertainty estimates in the current implementation. The code for training and inference of the model is available at https://github.com/epfl-radio-astro/diffusionRI.git

astro-ph.IM↗

arXiv2026

A Guided Unconditional Diffusion Model to Synthesize and Inpaint Radio Galaxies from FIRST, MGCLS and Radio Zoo

We present a masked-guided approach for a denoising diffusion probabilistic model (DDPM) trained to generate and inpaint realistic radio galaxy images. The inpainting capability is particularly relevant for reconstructing incomplete observations, improving downstream tasks such as source characterization and morphological classification. We train the model on a combination of the FIRST survey, Radio Galaxy Zoo, and cutouts from the MGCLS survey, enabling it to capture a broad range of radio galaxy morphologies across different observational regimes. We evaluate the realism of the generated samples through statistical comparisons with real data, ensuring consistency in key morphological and intensity distributions. Our unconditional model produces morphologically plausible galaxies while maintaining diversity, highlighting the suitability of diffusion models for this task. This approach provides a scalable alternative to computationally expensive simulations and enables effective data augmentation for machine learning applications in radio astronomy, including source detection, classification, and image reconstruction.

astro-ph.GA↗

arXiv2025

Solving the Cosmological Vlasov-Poisson Equations with Physics-Informed Kolmogorov-Arnold Networks

Cold dark matter (CDM) evolves as a collisionless fluid under the Vlasov-Poisson equations, but N-body simulations approximate this evolution by discretising the distribution function into particles, introducing discreteness effects at small scales. We present a physics-informed neural network approach that evolves CDM fields without any use of N-body data or methods, using a Kolmogorov-Arnold network (KAN) to model the continuous displacement field for one-dimensional halo collapse. Physical constraints derived from the Vlasov-Poisson equations are embedded directly into the loss function, enabling accurate evolution beyond the first shell crossing. The trained model achieves sub-percent errors on the residuals even after seven shell crossings and matches N-body results while providing a resolution-free representation of the displacement field. In addition, displacement errors do not grow over time, a very interesting feature with respect to N-body methods. It can also reconstruct initial conditions through backward evolution when sufficient final-state information is available. Although current runtimes exceed those of N-body methods, this framework offers a new route to high-fidelity CDM evolution without particle discretisation, with prospects for extension to higher dimensions.

astro-ph.CO↗

arXiv2025

SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving differential equations by integrating physical laws into the learning process. This work leverages PINNs to simulate gravitational collapse, a critical phenomenon in astrophysics and cosmology. We introduce the Schr\"odinger-Poisson informed neural network (SPINN) which solve nonlinear Schr\"odinger-Poisson (SP) equations to simulate the gravitational collapse of Fuzzy Dark Matter (FDM) in both 1D and 3D settings. Results demonstrate accurate predictions of key metrics such as mass conservation, density profiles, and structure suppression, validating against known analytical or numerical benchmarks. This work highlights the potential of PINNs for efficient, possibly scalable modeling of FDM and other astrophysical systems, overcoming the challenges faced by traditional numerical solvers due to the non-linearity of the involved equations and the necessity to resolve multi-scale phenomena especially resolving the fine wave features of FDM on cosmological scales.

astro-ph.CO↗

arXiv2024

Radio Halo Detection in MWA Data using Deep Neural Networks and Generative Data Augmentation

Detecting diffuse radio emission, such as from halos, in galaxy clusters is crucial for understanding large-scale structure formation in the universe. Traditional methods, which rely on X-ray and Sunyaev-Zeldovich (SZ) cluster pre-selection, introduce biases that limit our understanding of the full population of diffuse radio sources. In this work, we provide a possible resolution for this astrophysical tension by developing a machine learning (ML) framework capable of unbiased detection of diffuse emission, using a limited real dataset like those from the Murchison Widefield Array (MWA). We generate for the first time radio halo images using Wasserstein Generative Adversarial Networks (WGANs) and Denoising Diffusion Probabilistic Models (DDPMs), and apply them to train a neural network classifier independent of pre-selection methods. The halo images generated by DDPMs are of higher quality than those produced by WGANs. The diffusion-supported classifier with a multi-head attention block achieved the best average validation accuracy of 95.93% over 10 runs, using 36 clusters for training and 10 for testing, without further hyperparameter tuning. Using our classifier, we rediscovered 9/12 halos (75% detection rate) from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) Catalogue, and 5/8 halos (63% detection rate) from the Planck Sunyaev-Zeldovich Catalogue 2 (PSZ2) within the GaLactic and Extragalactic All-sky MWA (GLEAM) survey. In addition, we identify 11 potential new halos, minihalos, or candidates in the COSMOS field using XMM-chandra-detected clusters in GLEAM data. This work demonstrates the potential of ML for unbiased detection of diffuse emission and provides labeled datasets for further study.

astro-ph.GA↗

arXiv2024

Wavelet Scattering Networks for Identifying Radio Galaxy Morphologies

Classifying the morphologies of radio galaxies is important to understand their physical properties and evolutionary histories. A galaxy's morphology is often determined by visual inspection, but as survey size increases robust automated techniques will be needed. Deep neural networks are an attractive method for automated classification, but have many free parameters and therefore require extensive training data and are subject to overfitting and generalization issues. We explore hybrid classification methods using the scattering transform, the recursive wavelet decomposition of an input image. We analyse the performance of the scattering transform for the Fanaroff-Riley classification of radio galaxies with respect to CNNs and other machine learning algorithms. We test the robustness of the different classification methods with training data truncation and noise injection, and find that the scattering transform can offer competitive performance with the most accurate CNNs.

astro-ph.IM↗

arXiv2023

Quantum Radio Astronomy: Data Encodings and Quantum Image Processing

We explore applications of quantum computing for radio interferometry and astronomy using recent developments in quantum image processing. We evaluate the suitability of different quantum image representations using a toy quantum computing image reconstruction pipeline, and compare its performance to the classical computing counterpart. For identifying and locating bright radio sources, quantum computing can offer an exponential speedup over classical algorithms, even when accounting for data encoding cost and repeated circuit evaluations. We also propose a novel variational quantum computing algorithm for self-calibration of interferometer visibilities, and discuss future developments and research that would be necessary to make quantum computing for radio astronomy a reality.

astro-ph.IM↗

arXiv2023

BIPP: An efficient HPC implementation of the Bluebild algorithm for radio astronomy

The Bluebild algorithm is a new technique for image synthesis in radio astronomy which decomposes the sky into distinct energy levels using functional principal component analysis. These levels can be linearly combined to construct a least-squares estimate of the radio sky, i.e. minimizing the residuals between measured and predicted visibilities. This approach is particularly useful for deconvolution-free imaging or for scientific applications that need to filter specific energy levels. We present an HPC implementation of the Bluebild algorithm for radio-interferometric imaging: Bluebild Imaging++ (BIPP). The library features interfaces to C++, C and Python and is designed with seamless GPU acceleration in mind. We evaluate the accuracy and performance of BIPP on simulated observations of the upcoming Square Kilometer Array Observatory and real data from the Low-Frequency Array (LOFAR) telescope. We find that BIPP offers accurate wide-field imaging and has competitive execution time with respect to the interferometric imaging libraries CASA and WSClean for images with $\leq 10^6$ pixels. Furthermore, due to the energy level decomposition, images produced with BIPP can reveal information about faint and diffuse structures before any cleaning iterations. BIPP does not perform any regularization, but we suggest methods to integrate the output of BIPP with CLEAN. The source code of BIPP is publicly released.

astro-ph.IM↗

arXiv2023

Deep learning approach for identification of HII regions during reionization in 21-cm observations -- II. foreground contamination

The upcoming Square Kilometre Array Observatory (SKAO) will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination which pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift $z=7$ to $11$. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with $AUC=95\%$ is possible for $8 (10\, {\rm cMpc})^3$ at $z>9$, for follow-up studies with infrared/optical telescopes to detect these sources.

astro-ph.IM↗

arXiv2022

PMT: Power Measurement Toolkit

Efficient use of energy is essential for today's supercomputing systems, as energy cost is generally a major component of their operational cost. Research into "green computing" is needed to reduce the environmental impact of running these systems. As such, several scientific communities are evaluating the trade-off between time-to-solution and energy-to-solution. While the runtime of an application is typically easy to measure, power consumption is not. Therefore, we present the Power Measurement Toolkit (PMT), a high-level software library capable of collecting power consumption measurements on various hardware. The library provides a standard interface to easily measure the energy use of devices such as CPUs and GPUs in critical application sections.

cs.PF↗

arXiv2022

PINION: Physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

With the advent of the Square Kilometre Array Observatory (SKAO), scientists will be able to directly observe the Epoch of Reionization by mapping the distribution of neutral hydrogen at different redshifts. While physically motivated results can be simulated with radiative transfer codes, these simulations are computationally expensive and can not readily produce the required scale and resolution simultaneously. Here we introduce the Physics-Informed neural Network for reIONization (PINION), which can accurately and swiftly predict the complete 4-D hydrogen fraction evolution from the smoothed gas and mass density fields from pre-computed N-body simulation. We trained PINION on the C$^2$-Ray simulation outputs and a physics constraint on the reionization chemistry equation is enforced. With only five redshift snapshots and a propagation mask as a simplistic approximation of the ionizing photon mean free path, PINION can accurately predict the entire reionization history between $z=6$ and $12$. We evaluate the accuracy of our predictions by analysing the dimensionless power spectra and morphology statistics estimations against C$^2$-Ray results. We show that while the network's predictions are in good agreement with simulation to redshift $z>7$, the network's accuracy suffers for $z<7$ primarily due to the oversimplified propagation mask. We motivate how PINION performance can be drastically improved and potentially generalized to large-scale simulations.

astro-ph.CO↗

arXiv2022

Lightweight HI source finding for next generation radio surveys

Future deep HI surveys will be essential for understanding the nature of galaxies and the content of the Universe. However, the large volume of these data will require distributed and automated processing techniques. We introduce LiSA, a set of python modules for the denoising, detection and characterization of HI sources in 3D spectral data. LiSA was developed and tested on the Square Kilometer Array Science Data Challenge 2 dataset, and contains modules and pipelines for easy domain decomposition and parallel execution. LiSA contains algorithms for 2D-1D wavelet denoising using the starlet transform and flexible source finding using null-hypothesis testing. These algorithms are lightweight and portable, needing only a few user-defined parameters reflecting the resolution of the data. LiSA also includes two convolutional neural networks developed to analyse data cubes which separate HI sources from artifacts and predict the HI source properties. All of these components are designed to be as modular as possible, allowing users to mix and match different components to create their ideal pipeline. We demonstrate the performance of the different components of LiSA on the SDC2 dataset, which is able to find 95% of HI sources with SNR > 3 and accurately predict their properties.

astro-ph.IM↗