8 citations · 13 across the 5 of their papers we have counts for
5 papers
Robust and scalable simulation-based inference for gravitational wave signals with gaps
Ruiting Mao, Jeong Eun Lee, Matthew C. Edwards
The Laser Interferometer Space Antenna (LISA) data stream will inevitably contain gaps due to maintenance and environmental disturbances, introducing nonstationarities and spectral…
First-time assessment of glitch-induced bias and uncertainty in inference of extreme mass ratio inspirals
Amin Boumerdassi, Matthew C. Edwards, Avi Vajpeyi +1
This work investigates the impact of streams of transient, non-Gaussian noise artifacts or "glitches" on the parameter estimation of extreme mass ratio inspirals (EMRI) in the Lase…
A novel stacked hybrid autoencoder for imputing LISA data gaps
Ruiting Mao, Jeong Eun Lee, Matthew C. Edwards
The Laser Interferometer Space Antenna (LISA) data stream will contain gaps with missing or unusable data due to antenna repointing, orbital corrections, instrument malfunctions, a…
Generative adversarial network for stellar core-collapse gravitational waves
Tarin Eccleston, Matthew C. Edwards
We present a rapid stellar core-collapse waveform emulator built using a deep convolutional generative adversarial network (DCGAN). The DCGAN was trained on the Richers \textit{et…
Calibrating approximate Bayesian credible intervals of gravitational-wave parameters
Ruiting Mao, Jeong Eun Lee, Ollie Burke +3
Approximations are commonly employed in realistic applications of scientific Bayesian inference, often due to convenience if not necessity. In the field of gravitational-wave (GW)…