Accurate identification of the nature of signals in ground-based gravitational-wave interferometer data

The data from ground-based gravitational-wave interferometers has been used for direct astrophysical observations of transient signals for almost a decade. With this data, over 90 compact binary systems have been observed and studied via their gravitational-wave emission. These observations have, and continue to, provide new solutions to astrophysical questions. Gravitational waves can provide information about astrophysical systems that has previously been inaccessible through electromagnetic observations. The aim of this thesis is to outline some of the dangers of making incorrect assumptions about observed signals in gravitational-wave interferometer data. In Chapter 1 I provide a summary of gravitational-wave interferometer data; from the basic design of the interferometers, through the form of the data, and some methods can be used to manipulate the data for analysis. I then describe the types of signals that can be observed in this data, how these signals are studied, and what further analysis can be performed on the results of these studies. In Chapter 2 I calculate the probability that transient gravitational-wave signals will overlap with each other in the data. In the chapters that follow I outline the potential problems that time-overlapping transients will cause for the signal detection and analysis methods currently implemented by the LIGO-Virgo-KAGRA collaboration. Chapter 3 shows how the presence of a second, time-overlapping, binary black hole signal can cause inaccuracies in the estimation of the parameters of the other binary black hole system. I show how this problem manifests in the estimated parameter distributions for different relative parameters between two compact binary signals. In Chapters 4 and 5 I consider whether it is possible to detect these cases of time-overlapping transients with current methods, determining what cases will be missed without modifications to current algorithms. I also discuss possible modifications to these algorithms, and a separate bespoke method, designed to identify which detected signals contain time-overlapping transient signals. Chapter 6 presents an analysis of the data of a gravitational-wave interferometer for the purposes of a search for scalar dark matter signals. In this work, we produced extremely precise estimations of the noise floor of the interferometer. We used this to identify and reject possible candidate signals from scalar dark matter. Finally, in Chapter 7, I provide a summary of the key findings in each of the chapters of this thesis. This chapter includes recommendations of extensions and adaptations to the described investigations to expand and improve upon this work

P1_1 Resting On The Shoulders Of Giants

This paper investigates the concept of a ’World Turtle’ as imagined in Terry Pratchett’s Diskworld series. The giant astronomical elephants which stand upon the turtle’s shell support the Diskworld. By assuming that the elephants have the same anatomy as terrestrial elephants, the dimensions of these animals is found. Each of the elephants would have to be 230km tall to be able to support the mass of the disk. It was also found that the size of the elephants to support a flat Earth would be 420km tall. A relationship between the radius of a disk and the height of the elephant was also found

Best (but oft-forgotten) practices:the design, analysis, and interpretation of Mendelian randomization studies

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy

Use of Mendelian Randomization for Identifying Risk Factors for Brain Tumors

Gliomas are a group of primary brain tumors, the most common and aggressive subtype of which is glioblastoma. Glioblastoma has a median survival of just 15 months after diagnosis. Only previous exposure to ionizing radiation and particular inherited genetic syndromes are accepted risk factors for glioma; the vast majority of cases are thought to occur spontaneously. Previous observational studies have described associations between several risk factors and glioma, but studies are often conflicting and whether these associations reflect true casual relationships is unclear because observational studies may be susceptible to confounding, measurement error and reverse causation. Mendelian randomization (MR) is a form of instrumental variable analysis that can be used to provide supporting evidence for causal relationships between exposures (e.g., risk factors) and outcomes (e.g., disease onset). MR utilizes genetic variants, such as single nucleotide polymorphisms (SNPs), that are robustly associated with an exposure to determine whether there is a causal effect of the exposure on the outcome. MR is less susceptible to confounding, reverse causation and measurement errors as it is based on the random inheritance during conception of genetic variants that can be relatively accurately measured. In previous studies, MR has implicated a genetically predicted increase in telomere length with an increased risk of glioma, and found little evidence that obesity related factors, vitamin D or atopy are causal in glioma risk. In this review, we describe MR and its potential use to discover and validate novel risk factors, mechanistic factors, and therapeutic targets in glioma