1.3k citations · 1.6k across the 37 of their papers we have counts for
42 papers
The Potential Impact of Neuromorphic Computing on Radio Telescope Observatories
Nicholas J. Pritchard, Richard Dodson, Andreas Wicenec
Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and ne…
Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run
Damon Beveridge, Alistair McLeod, Linqing Wen +2
The detection of gravitational waves from compact binary coalescences has provided significant insights into our Universe, and the discovery of new and unique gravitational wave ca…
Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +2
Imminent radio telescope observatories provide massive data rates making deep learning based processing appealing while simultaneously demanding real-time performance at low-energy…
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
Nicholas J Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI tr…
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +1
Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spikin…
Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency In…