Publications by Emma Elizabeth Tolley

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 astro ...

2025

Radio halo detection in MWA data using deep neural networks and generative data augmentation

Jean-Paul Richard Kneib, Emma Elizabeth Tolley

Detecting diffuse radio emission, such as from haloes, 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 bia ...

OXFORD UNIV PRESS2025

S-R2D2: a spherical extension of the R2D2 deep neural network series paradigm for wide-field radio-interferometric imaging

Jean-Philippe Thiran, Jean-Paul Richard Kneib, Emma Elizabeth Tolley

Recently, the R2D2 paradigm, standing for ‘Residual-to-Residual DNN series for high-Dynamic-range imaging’, was introduced for image formation in Radio Interferometry (RI) as a learned version of the traditional algorithm CLEAN. The first incarnations of R ...

Oxford University Press (OUP)2025

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

Jean-Paul Richard Kneib, Sepand Kashani, Emma Elizabeth Tolley, Michele Bianco, Matthieu Martin Jean-André Simeoni

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 ...

2025

The stability of deep learning for 21cm foreground removal across various sky models and frequency-dependent systematics

Jean-Paul Richard Kneib, Daniel Felipe Forero Sanchez, Emma Elizabeth Tolley, Michele Bianco

Deep learning (DL) has recently been proposed as a novel approach for 21cm foreground removal. Before applying DL to real observations, it is essential to assess its consistency with established methods, its performance across various simulation models, an ...

2024

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

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Tianyue Chen, Michele Bianco

The upcoming Square Kilometre Array Observatory 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 ...

Oxford Univ Press2024

Quantum radio astronomy: Data encodings and quantum image processing

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Stefano Corda

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 reconstruc ...

Elsevier2024

SKA Science Data Challenge 2: analysis and results

Jean-Paul Richard Kneib, Xin Chen, Frédéric Courbin, Jean-Luc Starck, Emma Elizabeth Tolley, Yi Zhang, Lei Zhang, Hua Zhang, Xiuwei Zhang, Libo Yu

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of ad ...

OXFORD UNIV PRESS2023

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

Jean-Paul Richard Kneib, Emma Elizabeth Tolley, Michele Bianco

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 simula ...

OXFORD UNIV PRESS2023

Lightweight HI source finding for next generation radio surveys

Jean-Paul Richard Kneib, Frédéric Courbin, Emma Elizabeth Tolley, Aymeric Alexandre Galan, Austin Chandler Peel, Mark Thomas Sargent

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 modu ...

ELSEVIER2022