Jess McIver

Dr. Jess McIver, Canada Research Chair in Gravitational Wave Astrophysics, Assistant Professor

I lead the GW astrophysics group, the LIGO Scientific Collaboration group here at UBC, and the UBC-TRIUMF LISA group. I served as a co-chair of the LIGO Detector Characterization group, working at the interface between gravitational wave astrophysics and the LIGO detector instrumentation, from 2017-2020. Before I came to UBC as an assistant professor in 2019, I held a postdoctoral fellow position at the LIGO Laboratory at Caltech. I was based at the LIGO Livingston observatory during the first detection of gravitational waves in 2015, and I led the effort to validate this first detection as astrophysical. My research interests include gravitational-wave astrophysics with black holes, neutron stars, and core-collapse supernovae using detectors on Earth, like LIGO, as well as in space, like LISA. I’m also active in data science, machine learning, and characterization of large-scale physics experiment instrumentation.

Evan Goetz

Dr. Evan Goetz, Research Associate

My research areas of interest include astrophysics with gravitational waves from neutron stars and black holes, gravitational wave detector characterization and calibration, and developing analysis software tools to enable this science. I have been a member of the LIGO Scientific Collaboration for over 15 years, actively working on the detectors and analysis of LIGO data. I have developed new methods for analyzing data for continuous gravitational waves, increasing the accuracy and precision of detector calibration, and helped improve the quality of data from the LIGO detectors. I look forward to the transformational science that gravitational waves have to offer.

Man Leong (Mervyn) Chan

Dr. Man Leong (Mervyn) Chan, Postdoctoral Fellow

My work focuses on multi-messenger astronomy with gravitational wave and the application of machine learning algorithms to gravitational wave astronomy.  I am currently developing a low-latency annotation pipeline for the next LVK observing run based on a machine learning classifier, GWSkyNet, that aims to facilitate electromagnetic follow-up observations of gravitational wave candidates by determining whether the candidate is of astrophysical interest. I have also developed tools for optimizing follow-up observation strategies and estimating localization errors of gravitational wave sources as well as machine learning algorithm for the detection of gravitational wave signals. A LIGO member.

Alan Knee

Alan Knee, Ph.D. student

I work on parameter estimation of coalescing compact binaries via their emission of gravitational waves. The current focus of my research is looking at how the A+ upgrades to the LIGO detectors will help us resolve the relative spin orientations of binary black hole systems and the implications this has with respect to distinguishing between various formation channels.

Yannick (Niko) Lecoeuche, M.Sc. student

I am a member of the LIGO detector characterization team at UBC. My current research focuses on evaluating how non-Gaussian transient noise overlapping real gravitational wave signals affects parameter estimation for those signals. I have been part of the LIGO Scientific Collaboration for three years prior to my graduate studies, working as an operations specialist at the LIGO Hanford Observatory.

Katie Rink

Katie Rink, UBC alum

The primary focus of my research with the LIGO Detector Characterization group has been to investigate the effects of detector upgrades implemented throughout the third observing run (O3). Over the next few years I will also explore the impact of glitches on parameter estimation. For my masters research at UMass Dartmouth, I will be developing a discontinuous Galerkin solver for the Teukolsky equations to implement extreme mass ratio inspiral (EMRI) models into the SpECTRE code database.

Seraphim Jarov

Seraphim Jarov, B.Sc. student

The goal of my research is to improve our ability to safely distinguish between noise transients (glitches) and real astrophysical events in our detector data. My project is centered around Gravity Spy, a convolutional neural network used to identify different glitch types. I spend most of my time testing Gravity Spy’s performance by simulating waveforms and retraining Gravity Spy’s model on enriched training sets with the hope of improving classification accuracy for the next observing run.

Julian Ding, UBC alum

I recently graduated from a combined major in computer science and physics at UBC. My past research with the Gravitational Waves group focused on a model-free anomaly detection method for time series data called the Temporal Outlier Factor. Currently, I’m leading development on the Gravity Spy Convolutional Neural Network Decision Tree (GSpyNetTree), a multi-classifier machine learning model whose goal is to distinguish between real gravitational wave signals (chirps) and detector/terrestrial noise artifacts (glitches) by interpreting spectrogram images.

Helen Du, UBC alum

This summer I am continuing my honours thesis with the gravitational wave astrophysics group. My research project lies within the search for continuous gravitational waves from non-axisymmetric neutron stars, using more sensitive match-filtering based methods. This involves employing frequency tracking for a large number of candidates using Hidden Markov/Viterbi algorithms, followed by MCMC-based analyses with PyFStat. Then, comparisons can be made to current waveform models to look for potential continuous wave sources.

Kye Emond, Simon Fraser University student and NSERC USRA awardee

For my research project, I am investigating methods of data analysis for LISA, the upcoming space-based gravitational-wave observatory. More specifically, I am looking into extracting the parameters of binary systems emitting continuous gravitational waves from data with noise, glitches, and gaps. This data is simulated with glitch models developed with results from the LISA Pathfinder mission.

Annudesh Liyanage, UBC student and NSERC USRA awardee

I’m a UBC physics and math student. As part of the UBC LIGO GSpyNetTree team, my project focuses on generating the training sets for a convolutional neural network used to distinguish between glitches and signals. After having trained the model,  I will begin validation studies looking at how robust the algorithm’s predictions are to data with persistent noise features subtracted, exotic glitches not included in the training set, and signals from different sky positions and orientations.

Neev Shah, Indian Institute of Science Education & Research student and Mitacs Globalink Research Internship awardee

This summer, my work aims to develop a novel statistical distinguisher between real astrophysical signals and glitches in the LIGO data. I am creating a simulated astrophysical injection set spanning a wide range of parameter space in mass, spin, distance, etc. I will analyze their posteriors using Bilby and try to find regions of parameter space where posteriors of astrophysical signals can be distinguished from glitch posteriors

Vaibhav Garg, Delhi Technological University student and Mitacs Globalink Research Internship awardee

My research focuses on investigating new noise sources in the Advanced LIGO (aLIGO) detectors in order to mitigate and improve the ability of the detectors to prepare them for the next observing run (O4). My daily activities include looking at daily and hourly glitchgrams to search for abnormalities, generating omegascans to classify them into glitch classes and reporting them for further investigation and mitigation.

Sofía Álvarez López, Universidad de Los Andes student and Mitacs Globalink Research Internship awardee

I’m a Physics and Computer Science student from Universidad de Los Andes doing a Mitacs Research Internship at UBC. My project focuses on preparing the Gravity Spy Convolutional Neural Network Decision Tree (GSpyNetTree) pipeline to automatically distinguish between gravitational-wave signals and glitches using time-frequency visualizations of LIGO detector data for the next LVK observing run. After developing and training the CNN classifiers, I will start validation studies on the model’s performance on exotic glitches not included in the original training set and on spectrograms in which signals and glitches (or parts of them) simultaneously occur. These results will be crucial for adapting the current model architecture for the data detected in O4.


Group alumni

Dr. Miriam Cabero Müller, Postdoctoral fellow

My research with the team was focused on detecting compact binaries and studying black holes. I was a member of the LIGO Scientific Collaboration throughout my PhD, actively contributing to the detection of the first gravitational-wave signals and to the characterization of noise sources in data from the Advanced LIGO detectors. I have also worked on studying theoretical aspects of black-hole horizons and I have developed parameter estimation methods to analyze the remnant black hole in binary coalescences as tools to test General Relativity with gravitational waves. At UBC I explored machine learning techniques to optimize usage of telescope time in electromagnetic follow up of gravitational-wave candidates. Miriam is now a data scientist at EarthDaily Analytics in Vancouver. 

Nayyer Raza, M.Sc. alum

As a member of the LIGO Burst-Supernova team I studied the gravitational waves emitted during core-collapse supernovae: violent explosions of massive stars towards the end of their life. My research at UBC focused on using the Bayesian inference algorithm BayesWave to improve waveform reconstructions of the expected signals from supernovae in LIGO-Virgo data and learn about the dynamics of the astrophysical source. Nayyer is now a PhD student at McGill. 

Robert Beda, B.Sc. student

In the summer of 2020, Robert Beda was awarded an NSERC USRA to work with the UBC GW astrophysics group to understand the effects of different observatory system configurations on the quality of output data, as quantified by glitch rates. In particular, the standard reaction to approaching earthquakes changes the behaviour of seismic isolation systems so as to potentially influence data quality. Understanding this relationship may contribute towards development of observatory systems that collect even better data despite stressful environmental conditions. Github gwpy scripts

Maryum Sayeed

Maryum Sayeed, B.Sc. alum

Maryum Sayeed graduated from the University of British Columbia with a Combined Honours in Physics & Astronomy B.Sc. degree in May 2020 after working with the UBC GW astrophysics group on the impacts of non-stationarity data on astrophysical parameter estimation of compact binary coalescences. Maryum put her LIGO data analysis skills to work in the technology consulting sector in Alberta, and is now an Astronomy PhD student at Columbia University.

Sabiha Bhuiyan, B.Sc. alum

Sabiha Bhuiyan graduated from UBC with honours in May 2022, and will start graduate school at UBC in Fall 2022: My honours thesis research aimed at characterizing to what extent and how ground tilt induced by vertical ground motion from microseism at the LIGO detectors sites couples (via the control system) into horizontal differential motion between vacuum chambers of optical cavities. In particular, I explored how this coupling transfers from the ground to the optics through the stages of seismic isolation infrastructure. This involved transforming and visualizing data from the relevant sensors.

Nikolas TC Boily, B.Sc. alum

Nikolas Boily graduated from UBC’s Physics and Astronomy program in May 2022: My project characterized spin information of low-mass compact binary black hole mergers. Using a Bayesian inference pipeline, I analyzed our ability to measure the spin properties from computer-generated gravitational wave signals. This work will help future LIGO-Virgo observing runs by providing a statistical method characterizing these spin measurements. We hope the results of this work will bring us closer to understanding the formation of low-mass BBH systems, and possibly how stars can evolve into low mass binary black holes.

Sarah Thiele, B.Sc. alum

Sarah Thiele graduated from UBC with honours in May 2022, and will start graduate school at Columbia University in Fall 2022. My honours thesis with the team explored using the Temporal Outlier Factor to characterize LIGO data. I also worked with Dr. Jess McIver and the LIGO detector characterization team for the Fall 2020 term. The primary focus of my project was characterizing transient noise signals called “glitches” to create a veto which can differentiate between glitches and astrophysical signals. This involved investigating compact binary coalescence (CBC) parameter estimation on short-duration glitch sets, analyzing trends in waveform injections classified by a convolutional neural network called Gravity Spy, and other approaches to aid in forming a differentiation metric.