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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Blog Post number 4
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Blog Post number 3
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portfolio
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publications
A Study on the Characterization and Implementation of Tools for Advanced LIGO
Published in SMBHC Thesis Repository, 2016
This thesis covers two projects I worked on over the course of my undergraduate career, both under the umbrella of LIGO detector characterization.
Download here
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
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
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
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
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
talks
Characterization of the Omicron Trigger Generator and Transient analysis of aLIGOData
Published:
The Virgo and LIGO detectors aim at detecting gravitational wave (GW) signals produced by astrophysical sources. The burst group, in which the LAL group is involved, is looking for poorly-modeled and short-duration signals. To achieve this, members of the LAL group built search pipelines which are sensitive as well as robust against a large variety of detector glitches. The main obstacle to a GW burst detection is the data quality which is polluted by a great amount of detector glitch noise which mimics genuine burst signals. The LAL group is currently developing new tools to understand and characterize the detector’s glitch noise with a low latency. Mr. Gabbard helped characterize and improve one of these new tools (Omicron event trigger generator). Additionally, he helped to integrate this newly acquired information directly in burst search pipelines. He also assisted in Integrating these developments into an online-based analysis architecture and contributed to the development of new improvement ideas.
A low-latency Glitch Classification Algorithm Based in Waveform Morphology for Advanced LIGO
Published:
Through a fellowship funded by the National Science Foundation (NSF) Mr. Gabbard applied programming and mathematical skills for the analysis of gravitational wave data. Mr. Gabbard presented a novel and efficient machine learning algorithm for classification of signals that arise in gravitational wave channels of the Laser Interferometer Gravitational Wave Observatory (LIGO). Using data from LIGO’s sixth science run (S6), a new glitch classification algorithm based mainly on the morphology of the waveform as well as several other parameters (signal-to-noise ratio (SNR), duration, bandwidth, etc.) was developed.This was done using two novel methods, Kohonen Self Organizing Feature Maps (SOM), and discrete wavelet transform coefficients. The study showed the feasibility of utilizing SOMs to display a multidimensional trigger set in a low-latency two dimensional format.
The Birth of Gravitational-wave Astronomy
Published:
An outreach talk to the MS State University SPS group on graviational wave astronomy.
A low-latency Glitch Classification Algorithm Based in Waveform Morphology for Advanced LIGO
Published:
The Virgo and LIGO detectors aim at detecting gravitational wave (GW) signals produced by astrophysical sources. The burst group, in which the LAL group is involved, is looking for poorly-modeled and short-duration signals. To achieve this, members of the LAL group built search pipelines which are sensitive as well as robust against a large variety of detector glitches. The main obstacle to a GW burst detection is the data quality which is polluted by a great amount of detector glitch noise which mimics genuine burst signals. The LAL group is currently developing new tools to understand and characterize the detector’s glitch noise with a low latency. Mr. Gabbard helped characterize and improve one of these new tools (Omicron event trigger generator). Additionally, he helped to integrate this newly acquired information directly in burst search pipelines. He also assisted in Integrating these developments into an online-based analysis architecture and contributed to the development of new improvement ideas.
Genetic Programming Applied to Glitch Classification at LIGO
Published:
Quality improvement of interferometric data collected by gravitational-wave detectors such as Advanced LIGO and Virgo is mission critical for the success of gravitational-wave astrophysics. Gravitational-wave detectors are sensitive to a variety of disturbances of non-astrophysical origin with characteristic frequencies in the instrument band of sensitivity. Removing nonastrophysical artifacts that corrupt the data stream is crucial for increasing the number and statistical significance of gravitational-wave detections and enabling refined astrophysical interpretations of the data. Machine learning has proved to be a powerful tool for analysis of massive quantities of complex data in astronomy and related fields of study. We present two machine learning methods, based on random forest and genetic programming algorithms, that can be used to determine the origin of non-astrophysical transients in the LIGO detectors. We use two classes of transients with known instrumental origin that were identified during the first observing run of Advanced LIGO to show that the algorithms can successfully identify the origin of non-astrophysical transients in real interferometric data and thus assist in the mitigation of instrumental and environmental disturbances in gravitational-wave searches. While the datasets described in this paper are specific to LIGO, and the exact procedures employed were unique to the same, the random forest and genetic programming code bases and means by which they were applied as a dual machine learning approach are completely portable to any number of instruments in which noise is believed to be generated through mechanical couplings, the source of which is not yet discovered.
Machine Learning and Gravitational-wave Astronomy
Published:
We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.
Matching Matched Filtering with Deep Networks in Gravitational-wave Astronomy
Published:
We report on the construction of a deep convolutional neural network that can reproduce the sensitivity of a matched-filtering search for binary black hole gravitational-wave signals. The standard method for the detection of well-modeled transient gravitational-wave signals is matched filtering. We use only whitened time series of measured gravitational-wave strain as an input, and we train and test on simulated binary black hole signals in synthetic Gaussian noise representative of Advanced LIGO sensitivity. We show that our network can classify signal from noise with a performance that emulates that of match filtering applied to the same data sets when considering the sensitivity defined by receiver-operator characteristics.
teaching
Phys 108 Lab TA
Undergraduate Lab, University of Mississippi, Department of Physics and Astronomy, 2016
Instructed an introductory physics lab with over twenty students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.
A1 Lab TA
Undergraduate Lab, University of Glasgow, Department of Physics and Astronomy, 2018
Instructed an introductory astronomy lab with over 40 students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.
A2 Lab TA
Undergraduate Lab, University of Glasgow, Department of Physics and Astronomy, 2018
Instructed an astronomy lab with over 40 students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.
A3 Honors Lab TA
Undergraduate Lab, University of Glasgow, Department of Physics and Astronomy, 2019
Instructed an honors astronomy lab with over 40 students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.
Introductory Machine Learning Course
Workshop, University of Glasgow, Department of Physics and Astronomy, 2019
A hands-on machine learning course for physics and astronomy PhD students at the University of Glasgow (see link below for course).