Gravity theories, black holes and compact objects.

Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

Unveiling Microlensing Biases in Testing General Relativity with Gravitational Waves

Gravitational waves (GW) from chirping binary black holes (BBHs) provide unique opportunities to test general relativity (GR) in the strong-field regime. However, testing GR can be challenging when incomplete physical modeling of the expected signal gives rise to systematic biases. In this study, we investigate the potential influence of wave effects in gravitational lensing (which we refer to as microlensing) on tests of GR using GWs for the first time. We utilize an isolated point-lens model for microlensing with the lens mass ranging from $10-10^5~$M$_\odot$ and base our conclusions on an astrophysically motivated population of BBHs in the LIGO-Virgo detector network. Our analysis centers on two theory-agnostic tests of gravity: the inspiral-merger-ringdown consistency test (IMRCT) and the parameterized tests. Our findings reveal two key insights: First, microlensing can significantly bias GR tests, with a confidence level exceeding $5\sigma$. Notably, substantial deviations from GR $(\sigma > 3)$ tend to align with a strong preference for microlensing over an unlensed signal, underscoring the need for microlensing analysis before claiming any erroneous GR deviations. Nonetheless, we do encounter scenarios where deviations from GR remain significant ($1 < \sigma < 3$), yet the Bayes factor lacks the strength to confidently assert microlensing. Second, deviations from GR correlate with pronounced interference effects, which appear when the GW frequency ($f_\mathrm{GW}$) aligns with the inverse time delay between microlens-induced images ($t_\mathrm{d}$). These false deviations peak in the wave-dominated region and fade where $f_\mathrm{GW}\cdot t_\mathrm{d}$ significantly deviates from unity. Our findings apply broadly to any microlensing scenario, extending beyond specific models and parameter spaces, as we relate the observed biases to the fundamental characteristics of lensing.Comment: 21 pages, 12 figure

Global structure of Black Holes via dynamical system

We recast the system of Einstein field equations for Locally Rotationally Symmetric spacetimes into an autonomous system of covariantly defined geometrical variables. The analysis of this autonomous system gives all the important global features of the maximal extension of these spacetimes. We conclude that the dynamical system analysis can be a powerful mathematical tool for qualitative understanding of the global structure of spacetimes covariantly, without actually solving the field equations.Comment: 13 pages, 9 figure

A Comprehensive and Modularized Platform for Time Series Forecast and Analytics

Users that work with time series data typically disaggregate time series problems into various isolated tasks and use specific libraries, packages, tools, and services that deal with each individual task. However, the tools used are often fragmented. Analysts have to load different packages for common tasks such as data preprocessing, clustering, feature extraction, forecasting, hierarchical reconciliation, evaluation, and visualization. This disclosure describes a reliable, scalable infrastructure to meet various needs of time series practitioners without adding engineering overload. The infrastructure is modularized and the modules are connected in a flow type declarative language which makes the infrastructure extensible and future proof. Practitioners can use the entire infrastructure or only certain modules, while performing other operations using first or third party libraries or pipelines