Shirsha Mukherjee

Shirsha Mukherjee

he/him

I'm a rising junior at the University of Oklahoma! I'm double majoring in Math & Physics, and love to train martial arts and play guitar. I've conducted research in a variety of areas from biomedical engineering, to engineering pathways, to gravitational-wave analysis.

CIERA REU Research

With the release of the fourth Gravitational-Wave Transient Catalog (GWTC-4), observations from the first four observing runs (O1–O4) of the LIGO–Virgo–KAGRA Collaboration provide an unprecedented dataset of compact binary mergers detected by the Laser Interferometer Gravitational-Wave Observatory and partner detectors. These observations include dozens of merger events involving binary black holes (BBHs), binary neutron stars, and neutron star–black hole systems.

Gravitational waves are ripples in spacetime produced by the acceleration of massive objects. When two compact objects—such as black holes or neutron stars—orbit one another and eventually merge, their intense gravitational fields distort spacetime and generate waves that propagate outward across the universe. Sensitive interferometers such as LIGO and its partner detectors measure these signals as they pass through Earth.

In this project, I focus specifically on the population of binary black hole mergers. By analyzing the ensemble of detected BBH events, we can infer statistical properties of the broader BBH population throughout the universe. Understanding the distribution of parameters such as mass ratios provides important clues about how these systems form. For example, binaries formed through isolated stellar evolution tend to produce nearly equal-mass systems, while dynamical formation in dense stellar environments can generate more extreme mass ratios.

To study these populations, I use hierarchical Bayesian inference, a statistical framework that combines information from many individual merger events to infer population-level parameters. Each detected gravitational-wave signal provides posterior distributions for the properties of that event. By combining these event-level posteriors with a model of detector selection effects, hierarchical inference allows us to estimate the underlying distribution of binary black hole properties across the universe.

This approach enables us to move beyond studying individual mergers and instead understand the astrophysical processes shaping the population as a whole.