110 citations · 667 across the 12 of their papers we have counts for
12 papers
Star Cluster Classification using Deep Transfer Learning with PHANGS-HST
Stephen Hannon, Bradley C. Whitmore, Janice C. Lee +17
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming natur…
Star Cluster Classification in the PHANGS-HST Survey: Comparison between Human and Machine Learning Approaches
Bradley C. Whitmore, Janice C. Lee, Rupali Chandar +21
When completed, the PHANGS-HST project will provide a census of roughly 50,000 compact star clusters and associations, as well as human morphological classifications for roughly 20…
Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
E. A. Huerta, Asad Khan, Xiaobo Huang +10
The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astro…
Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
Wei Wei, E. A. Huerta, Mengshen Yun +5
We present the first application of deep learning forecasting for binary neutron stars, neutron star - black hole systems, and binary black hole mergers that span an eccentricity r…
Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers
Wei Wei, Asad Khan, E. A. Huerta +2
We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent ne…
Deep learning for gravitational wave forecasting of neutron star mergers
Wei Wei, E. A. Huerta
We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real…