22 citations · 37 across the 5 of their papers we have counts for
5 papers
Probabilistic kilonova prediction from gravitational wave inferred binary neutron star parameters
Xiao-Fei Dong, Ik Siong Heng, Gavin P. Lamb +10
Kilonovae provide a key electromagnetic window into binary neutron star mergers, revealing the properties of the merging system while probing r-process nucleosynthesis of the Unive…
Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation
Thomas Wallace, Ik Siong Heng, Senad Subasic +1
Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthet…
Identifying and Mitigating Machine Learning Biases for the Gravitational Wave Detection Problem
Narenraju Nagarajan, Christopher Messenger
Matched filtering is a long-standing technique for the optimal detection of known signals in stationary Gaussian noise. However, it has known departures from optimality when operat…
Applications of machine learning in gravitational wave research with current interferometric detectors
Elena Cuoco, Marco Cavaglià, Ik Siong Heng +2
This article provides an overview of the current state of machine learning in gravitational-wave research with interferometric detectors. Such applications are often still in their…
Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State
Qian Hu, Jessica Irwin, Qi Sun +4
Gravitational waves (GWs) from binary neutron stars (BNSs) offer valuable understanding of the nature of compact objects and hadronic matter, and the science potential will be grea…