11 papers
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…
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…
Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids
Alexander Williamson, Richard Dodson, Pascal J. Elahi +10
The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude more antennas and larger bandwidth. Foremost…
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…
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…
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…