Dr. Hunter Gabbard

Dr. Hunter Gabbard

Lead Technologist at Booz Allen Hamilton, expert in physics and machine learning.

Publications

Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy

Published in Nature Physics, 2021

Using conditional variational autoencoders we are able to reproduce the Bayesian posterior for several simulated GW events. We compare our results to standard Bayesian inference techniques from the Bilby Bayesian inference library and are able to achieve ~6 orders of magnitude speed-up in performance.

Recommended citation: Gabbard, H., Messenger, C., Heng, I.S. et al. Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. Nat. Phys. 18, 112–117 (2022). https://doi.org/10.1038/s41567-021-01425-7 https://www.nature.com/articles/s41567-021-01425-7

Ground motion prediction at gravitational wave observatories using archival seismic data

Published in Classical and Quantum Gravity, 2019

We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach.

Recommended citation: Mukund, Nikhil, Michael Coughlin, Jan Harms, Sebastien Biscans, Jim Warner, Arnaud Pele, Keith Thorne, et al. “Ground Motion Prediction at Gravitational Wave Observatories Using Archival Seismic Data.” Classical and Quantum Gravity 36, no. 8 (January 2019): 085005. https://doi.org/10.1088/1361-6382/ab0d2c. https://iopscience.iop.org/article/10.1088/1361-6382/ab0d2c/meta

Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy

Published in Physical Review Letters, 2018

Using a deep convolutional neural network, we show for the first time that deep learning can match the efficiency of the gold-standard LIGO signal detection techniques.

Recommended citation: Gabbard, Hunter, Michael Williams, Fergus Hayes, and Chris Messenger. “Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy.” Physical Review Letters 120, no. 14 (June 2018). https://doi.org/10.1103/physrevlett.120.141103. https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.141103

Control strategy to limit duty cycle impact of earthquakes on the LIGO gravitational-wave detectors

Published in Classical and Quantum Gravity, 2018

This paper describes a control strategy to use this early-warning system to reduce the LIGO downtime by  ~30%. It also presents a plan to implement this new earthquake configuration in the LIGO automation system.

Recommended citation: Biscans, S, J Warner, R Mittleman, C Buchanan, M Coughlin, M Evans, H Gabbard, et al. “Control Strategy to Limit Duty Cycle Impact of Earthquakes on the LIGO Gravitational-Wave Detectors.” Classical and Quantum Gravity 35, no. 5 (2018): 055004. https://doi.org/10.1088/1361-6382/aaa4aa. https://iopscience.iop.org/article/10.1088/1361-6382/aaa4aa/meta

Limiting the effects of earthquakes on gravitational-wave interferometers

Published in Classical and Quantum Gravity, 2017

By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40 to 100 earthquake events in a 6-month time-period.

Recommended citation: Coughlin, Michael, Paul Earle, Jan Harms, Sebastien Biscans, Christopher Buchanan, Eric Coughlin, Fred Donovan, et al. “Limiting the Effects of Earthquakes on Gravitational-Wave Interferometers.” Classical and Quantum Gravity 34, no. 4 (February 2017): 044004. https://doi.org/10.1088/1361-6382/aa5a60. https://iopscience.iop.org/article/10.1088/1361-6382/aa5a60/pdf