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20132024
most citedGW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence

2.2k citations · 3.7k across the 28 of their papers we have counts for

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Showing astro-ph.IMShow all

6 papers · 1 filter

astro-ph.IM2019★ 71 cited

Preliminary study on parameter estimation accuracy of supermassive black hole binary inspirals for TianQin

Wen-Fan Feng, Hai-Tian Wang, Xin-Chun Hu +2

We use the Fisher information matrix method to calculate the parameter estimation accuracy of inspiraling supermassive black holes binaries for TianQin, a space-borne laser interfe…

astro-ph.IM2018★ 73 cited

Optimizing searches for electromagnetic counterparts of gravitational wave triggers

Michael W. Coughlin, Duo Tao, Man Leong Chan +9

With the detection of a binary neutron star system and its corresponding electromagnetic counterparts, a new window of transient astronomy has opened. Due to the size of the error…

astro-ph.IM2017★ 35 cited

Systematic errors in estimation of gravitational-wave candidate significance

Collin Capano, Thomas Dent, Chad Hanna +4

The statistical significance of a candidate gravitational-wave (GW) event is crucial to the prospects for a confirmed detection, or for its selection as a candidate for follow-up e…

astro-ph.IM2016★ 32 cited

Systematic errors in estimation of gravitational-wave candidate significance

Collin Capano, Thomas Dent, Chad Hanna +4

We investigate the issue in determining the significance of candidate transient gravitational-wave events in a ground-based interferometer network. Given the presence of non-Gaussi…

astro-ph.IM2014★ 8 cited

Global Optimization for Future Gravitational Wave Detectors' Sites

Yi-Ming Hu, Peter Raffai, Laszlo Gondan +5

We consider the optimal site selection of future generations of gravitational wave detectors. Previously, Raffai et al. optimized a 2-detector network with a combined figure of mer…

astro-ph.IM2014★ 2 cited

Efficient Exploration of Multi-Modal Posterior Distributions

Yi-Ming Hu, Martin Hendry, Ik Siong Heng

The Markov Chain Monte Carlo (MCMC) algorithm is a widely recognised as an efficient method for sampling a specified posterior distribution. However, when the posterior is multi-mo…