Testing General Relativity and Probing Nuclear Physics in the era of Gravitational Waves

The LIGO and Virgo detectors have detected 90 gravitational wave events so far. The gravitational waves have come from pairs of black holes or neutron stars. The astrophysical interpretation of these events was made possible by sophisticated modeling of both source and detector noise. In this thesis, I develop and apply model-independent methods to check for biases due to un-modeled astrophysical or terrestrial effects and to search for signals from the remnant of binary neutron star coalescences. The methods use a sine-Gaussian wavelet basis set to reconstruct the gravitational waveform which is sensitive to a wide range of waveform morphologies and enables us to increase detection confidence and improve data quality. I report the results of applying these methods to the first three observing runs of LIGO and Virgo. I also characterize these methods using various simulation studies.Ph.D

Reconstructing gravitational wave signals from binary black hole mergers with minimal assumptions

We present a systematic comparison of the binary black hole (BBH) signal waveform reconstructed by two independent and complementary approaches used in LIGO and Virgo source inference: a template-based analysis, and a morphology-independent analysis. We apply the two approaches to real events and to two sets of simulated observations made by adding simulated BBH signals to LIGO and Virgo detector noise. The first set is representative of the 10 BBH events in the first Gravitational Wave Transient Catalog (GWTC-1). The second set is constructed from a population of BBH systems with total mass and signal strength in the ranges that ground based detectors are typically sensitive. We find that the reconstruction quality of the GWTC-1 events is consistent with the results of both sets of simulated signals. We also demonstrate a simulated case where the presence of a mismodelled effect in the observed signal, namely higher order modes, can be identified through the morphology-independent analysis. This study is relevant for currently progressing and future observational runs by LIGO and Virgo

The BayesWave analysis pipeline in the era of gravitational wave observations

We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and non-stationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler

Detection and parameter estimation of binary neutron star merger remnants

Detection and parameter estimation of binary neutron star merger remnants can shed light on the physics of hot matter at supranuclear densities. Here we develop a fast, simple model that can generate gravitational waveforms, and show it can be used for both detection and parameter estimation of post-merger remnants. The model consists of three exponentially-damped sinusoids with a linear frequency-drift term. The median fitting factors between the model waveforms and numerical-relativity simulations exceed 0.90. We detect remnants at a post-merger signal-to-noise ratio of ≥7 using a Bayes-factor detection statistic with a threshold of 3000. We can constrain the primary post-merger frequency to ±^(1.4)_(1.2)% at post-merger signal-to-noise ratios of 15 with an increase in precision to ±^(0.3)_(0.2)% for post-merger signal-to-noise ratios of 50. The tidal coupling constant can be constrained to ±⁹₁₂% at post-merger signal-to-noise ratios of 15, and ±5% at post-merger signal-to-noise ratios of 50 using a hierarchical inference model

Detection and parameter estimation of binary neutron star merger remnants

Detection and parameter estimation of binary neutron star merger remnants can shed light on the physics of hot matter at supranuclear densities. Here we develop a fast, simple model that can generate gravitational waveforms, and show it can be used for both detection and parameter estimation of post-merger remnants. The model consists of three exponentially-damped sinusoids with a linear frequency-drift term. The median fitting factors between the model waveforms and numerical-relativity simulations exceed 0.90. We detect remnants at a post-merger signal-to-noise ratio of $\ge 7$ using a Bayes-factor detection statistic with a threshold of 3000. We can constrain the primary post-merger frequency to $\pm_{1.2}^{1.4}\%$ at post-merger signal-to-noise ratios of 15 with an increase in precision to $\pm_{0.2}^{0.3}\%$ for post-merger signal-to-noise ratios of 50. The tidal coupling constant can be constrained to $\pm^{9}_{12}\%$ at post-merger signal-to-noise ratios of 15, and $\pm 5\%$ at post-merger signal-to-noise ratios of 50 using a hierarchical inference model

Characterizing the efficacy of methods to subtract terrestrial transient noise near gravitational wave events and the effects on parameter estimation

We investigate the impact of transient noise artifacts, or {\it glitches}, on gravitational wave inference, and the efficacy of data cleaning procedures in recovering unbiased source properties. Due to their time-frequency morphology, broadband glitches demonstrate moderate to significant biasing of posterior distributions away from true values. In contrast, narrowband glitches have negligible biasing effects owing to distinct signal and glitch morphologies. We inject simulated binary black hole signals into data containing three common glitch types from past LIGO-Virgo observing runs, and reconstruct both signal and glitch waveforms using {\tt BayesWave}, a wavelet-based Bayesian analysis. We apply the standard LIGO-Virgo-KAGRA deglitching procedure to the detector data - we subtract the glitch waveform estimated by the joint {\tt BayesWave} inference before performing parameter estimation with detailed compact binary waveform models. We find that this deglitching effectively mitigates bias from broadband glitches, with posterior peaks aligning with true values post deglitching. This provides a baseline validation of existing techniques, while demonstrating waveform reconstruction improvements to the Bayesian algorithm for robust astrophysical characterization in glitch-prone detector data.Comment: 22 pages, 17 figure

The First Gravitational Wave Catalog, GWTC-1

Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), January 27-28, 2020, Georgia Tech Global Learning Center, Atlanta, GA.The Career, Research, and Innovation Development Conference (CRIDC) is designed to equip on-campus and online graduate students with tools and knowledge to thrive in an ever-changing job market.Sudarshan Ghonge, in the School of Physics at Georgia Tech, was the winner of a College of Science Travel Award.On September 14, 2015, the earth was witness to one of universe’s loudest cataclysmic events: the collision of two black holes. The collision resulted in a perturbation in the very fabric of space time - a Gravitational Wave (GW). This event was brighter than all the stars in the universe combined. The effect of a GW manifests as a change in the lengths of objects. However, due to the weakly coupling nature nature of gravity, these length changes are miniscule, with strain amplitudes of the order of 10-21. Two extremely precise measuring instruments in Hanford, Washington and Livingston, Louisiana known as the Laser Interferometer Gravitational-Wave Observatories (LIGO) observed this event with a high significance. Code named GW150914, it was the first ever detection of GWs, a phenomenon predicted by Einstein’s theory of General Relativity. In summer 2017, LIGO was joined by Virgo, a similar interferometric detector in Pisa, Italy. Collectively, LIGO and Virgo have observed ten such GW events with high significances and these were recently published as the first comprehensive Gravitational Wave Transient Catalog or GWTC-1. GWTC-1 showcases Binary Black Hole (BBH) systems which cover large regions of parameter space. The total mass varies between 18 to 85 solar masses and distances vary from 320 to 2750 Megaparsec. It also includes systems with high spin and mass ratios which fold in interesting physics. I present the results from the catalog along with the inferred Astrophysics such as rates, formation channels and tests of General Relativity.National Science Foundation (U.S.) - PHY-0757058, PHY-0823459, PHY 1806580, PHY 1809572, TG-PHY12001