|MIT Department: Physics
Faculty Mentor: Prof. Salvatore Vitale
Undergraduate Institution: New Jersey Institute of Technology, Newark
Website: University Feature
I am Divjyot, a rising senior pursuing Bachelor of Science honors with a double major in applied physics and applied math. I aspire to pursue a PhD in theoretical astrophysics and go on to be a researcher in academia. Using my STEM skills, I want to help humanity by improving our understanding of the universe, increase diversity, and mentoring future generations. As an undergraduate, I have conducted research projects in several disciplines. I have designed computer models to study the impact of soot on global warming, investigated the correlation between Chronic Obstructive Pulmonary Disease and air pollution, and analyzed the dependence of blender efficiency on its physical dimensions. These projects have been conducted with New Jersey Institute of Technology, NJ Governor’s STEM Scholars Program, and Oxford University, respectively. In my free time, I enjoy talking to new people and being with nature.
How do Black Holes Form? Developing a Statistical Tool to Analyze Gravitational Waves with Future Detectors
Divjyot Singh1,2, Kwan-Yeung Ng3,4, Salvatore Vitale3,4
1Department of Physics, New Jersey Institute of Technology
2Department of Mathematical Sciences, New Jersey Institute of Technology
3LIGO Laboratory, Massachusetts Institute of Technology
4Kavli Institute for Astrophysics and Space Research,
Massachusetts Institute of Technology
Black holes (BHs) allow us to probe and understand the extremes of the universe. The collision of two BHs as a Binary Black Hole merger (BBH) creates detectable gravitational waves (GWs), which are disturbances in the space-time continuum. Detectors like LIGO allow us to observe these GW signals, which can provide information about the properties of BHs like mass and formation channels. Future generation GW detectors will dramatically increase the range of BBH data we receive, providing access to the early universe. In this project, we simulated BBH data from different formation channels- population III stars (PopIII) and primordial black holes (PBHs)- using population synthesis analysis. We then created two statistical models based on Bayesian data analysis: one with PopIII and PBH and one with only PopIII. We calculated the likelihood of both the models on the simulated data and observed a higher likelihood for PopIII and PBH. Hence, we concluded that this statistical technique can successfully identify the presence of PBHs in BBH data. This project provides an alternate method to explore the existence of PBHs.