902 citations · 13.3k across the 140 of their papers we have counts for
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VeSPA: The SuperWASP Variable Star Photometry Archive
Adam McMaster, Andrew J. Norton, Hugh J. Dickinson +2
We present the first results from the SuperWASP Variable Stars (SVS) citizen science project. The photometry archive of the Wide Angle Search for Planets has previously been search…
Identification of single spectral lines in large spectroscopic surveys using UMLAUT: an Unsupervised Machine Learning Algorithm based on Unbiased Topology
I. Baronchelli, C. M. Scarlata, L. Rodriguez-Muñoz +19
The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, m…
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Mike Walmsley, Anna M. M. Scaife, Chris Lintott +9
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to an…
Identifying muon rings in VERITAS data using convolutional neural networks trained on images classified with Muon Hunters 2
Kevin Flanagan, John Quinn, Darryl Wright +4
Muons from extensive air showers appear as rings in images taken with imaging atmospheric Cherenkov telescopes, such as VERITAS. These muon-ring images are used for the calibration…
Predicting the self-lensing population in optical surveys
Grzegorz Wiktorowicz, Matthew Middleton, Norman Khan +3
The vast majority of binaries containing a compact object and a regular star spend most of their time in a quiescent state where no strong interactions occur between components. De…
Super-resolving Herschel imaging: a proof of concept using Deep Neural Networks
Lynge Lauritsen, Hugh Dickinson, Jane Bromley +4
Wide-field sub-millimetre surveys have driven many major advances in galaxy evolution in the past decade, but without extensive follow-up observations the coarse angular resolution…