Hi, I’m Veome Kapil

PhD student in Gravitational Wave Astrophysics

I am a graduate student working in Prof. Emanuele Berti's group at Johns Hopkins University. My interests include astrophysical modeling of Double Compact Object (DCO) populations, as well as possibilities for detection by next-generation gravitational wave detectors. I am also an active developer of COMPAS, an open source code for producing rapid simulations of single and binary star systems.

When I'm not working, I enjoy doing data science experiments, building Arduino projects, playing the guitar, and taking naps.

Project Highlights

Calibration of neutron star natal kick velocities to isolated pulsar observations

Current prescriptions for supernova natal kicks in rapid binary population synthesis simulations are based on fits of simple functions to single pulsar velocity data. We explore a new parameterization of natal kicks received by neutron stars in isolated and binary systems developed by Mandel & Müller, which is based on 1D models and 3D supernova simulations, and accounts for the physical correlations between progenitor properties, remnant mass, and the kick velocity. We constrain two free parameters in this model using very long baseline interferometry velocity measurements of Galactic single pulsars. We find that the inferred values of natal kick parameters do not differ significantly between single and binary evolution scenarios. The best-fit values of these parameters are v_ns = 520 km/s for the scaling pre-factor for neutron star kicks, and σ_ns = 0.3 for the fractional stochastic scatter in the kick velocities.

Systematic bias from waveform modeling for binary black hole populations in next-generation gravitational wave detectors

Next-generation gravitational wave detectors such as the Einstein Telescope and Cosmic Explorer will have increased sensitivity and observing volumes, enabling unprecedented precision in parameter estimation. However, this enhanced precision could also reveal systematic biases arising from waveform modeling, which may impact astrophysical inference. We investigate the extent of these biases over a year-long observing run with $10^{5}$ simulated binary black hole sources using the linear signal approximation. To establish a conservative estimate, we sample binaries from a smoothed truncated power-law population model and compute systematic parameter biases between the IMRPhenomXAS and IMRPhenomD waveform models. For sources with signal-to-noise ratios above 100, we estimate statistically significant parameter biases in $\sim 3\%-20\%$ of the events, depending on the parameter. We find that the average mismatch between waveform models required to achieve a bias of $\leq 1\sigma$ for $99\%$ of detections with signal-to-noise ratios $\geq 100$ should be $\mathcal{O}(10^{-5})$ , or at least one order of magnitude better than current levels of waveform accuracy.