|I lead the GW astrophysics group and the LIGO Scientific Collaboration group here at UBC. I currently serve as a co-chair of the LIGO Detector Characterization group, working at the interface between gravitational wave astrophysics and the LIGO detector instrumentation. 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.|
|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.|
|My research focuses 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. Currently at UBC I am exploring machine learning techniques to optimize usage of telescope time in electromagnetic follow up of gravitational-wave candidates.|
|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.|
|As a member of the LIGO Burst-Supernova team I study the gravitational waves emitted during core-collapse supernovae: violent explosions of massive stars towards the end of their life. My research focuses 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.|
|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.|
|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.|
|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.|
|I am an undergrad going into my final year in a combined major in computer science and physics. My research with the Gravitational Waves group focuses on unsupervised methods of detecting time series anomalies in gravitational wave data, with the ultimate goal that such methods can be used to reliably detect novel, unclassified transient noise events (glitches). The current method of interest is the Temporal Outlier Factor, a technique that uses higher-dimensional embedding to recreate the dynamical phase space of a time series and then correlates spatial and temporal clustering to find anomalies in the data.|
|I 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.|
|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 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. She is putting her LIGO data analysis skills to work in the technology consulting sector in Alberta.|
- Most members of the UBC GW astrophysics group are part of the LIGO group at UBC, which spans six labs across the faculties of science and applied science.
- The UBC LIGO group is part of the LIGO Scientific Collaboration
- Some UBC GW astro members are members of the LISA Consortium
- Some of our colleagues at UBC research lower frequency gravitational-wave astronomy with Pulsar Timing Arrays.