17 citations · 17 across the 1 of their papers we have counts for
5 papers
Predicting CME Arrivals with Heliospheric Imagers from L5: A Data Assimilation Approach
Tanja Amerstorfer, Justin Le Louëdec, David Barnes +5
The Solar TErrestrial RElations Observatory (STEREO) mission has laid a foundation for advancing real-time space weather forecasting by enabling the evaluation of heliospheric imag…
Solar Transient Recognition Using Deep Learning (STRUDL) for heliospheric imager data
Maike Bauer, Justin Le Louëdec, Tanja Amerstorfer +3
Coronal Mass Ejections (CMEs) are space weather phenomena capable of causing significant disruptions to both space- and ground-based infrastructure. The timely and accurate detecti…
Beacon2Science: Enhancing STEREO/HI beacon data with machine learning for efficient CME tracking
Justin Le Louëdec, Maike Bauer, Tanja Amerstorfer +1
Observing and forecasting coronal mass ejections (CME) in real-time is crucial due to the strong geomagnetic storms they can generate that can have a potentially damaging effect, f…
First observations of a geomagnetic superstorm with a sub-L1 monitor
Eva Weiler, Christian Möstl, Emma E. Davies +10
Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component o…
Deep learning investigation for chess player attention prediction using eye-tracking and game data
Justin Le Louedec, Thomas Guntz, James Crowley +1
This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this…