Research

Gravitational Wave Detection Algorithms

My research has focused on developing advanced algorithms for detecting gravitational wave transients using data from interferometric detectors like LIGO and Virgo. One of the key tools I have worked with is the coherent WaveBurst (cWB) algorithm, which is designed for detecting unmodeled gravitational wave events. I have contributed to optimizing the sensitivity of this algorithm using machine learning, which is detailed in this study.

Furthermore, I have also contributed to refining detection strategies for specific transient sources like binary black hole mergers and core-collapse supernovae. One such example is an optically targeted search for gravitational waves emitted by core-collapse supernovae during LIGO’s third observing run.

Intermediate-Mass Black Hole Binaries and Eccentric Binary Searches

A significant part of my research has been dedicated to investigating gravitational wave signals from intermediate-mass black hole binaries and eccentric binary black hole mergers, which are relatively exotic and rare systems. My contributions include the detection of such systems in the Advanced LIGO and Virgo data. Notable findings include the analysis of GW190521 as a highly eccentric black hole merger, and the identification of binary black holes in the pair-instability mass gap, as presented in this paper.

Additionally, I was involved in a search for eccentric black hole coalescence during LIGO’s third observing run, enhancing our understanding of these unique gravitational wave events.

Machine Learning for Gravitational Wave Science

In recent years, I have focused on applying machine learning techniques to enhance gravitational wave detection and data analysis. This includes improving the sensitivity of detection algorithms, such as the cWB pipeline, by integrating machine learning. One example of this work is discussed in this publication, where we optimized model-independent searches for gravitational waves.

I have also contributed to machine learning-based noise classification strategies, which enhance the detection of gravitational wave events from binary black hole mergers and eccentric binaries, as detailed in this paper.

Noise Veto Techniques

An essential aspect of gravitational wave detection is separating genuine signals from instrumental noise. My work has included the development of noise veto techniques, which are critical for improving the reliability of gravitational wave detections. This includes the application of Gaussian mixture models to differentiate between noise and signals, which has significantly improved the accuracy of transient searches, as described in this paper.

I have also been involved in the study of noise artifacts and their impact on searches for intermediate-mass black holes, as explored in this publication.