On the Formation of GW190521-like Binary Black Hole Merger Systems

GW190521 is the most massive merging binary black hole (BBH) system detected so far. At least one of the component BHs was measured to lie within the pair-instability supernova (PISN) mass gap ($\sim 50-135\;{\rm M}_{\odot}$), making its formation a mystery. However, the transient observed signal allows alternative posterior distributions. There was suggestion that GW190521 could be an intermediate-mass ratio inspiral (IMRI), with the component masses $m_1\sim 170\;{\rm M}_{\odot}$ and $m_2\sim 16 \;{\rm M}_{\odot}$, happening to straddle the PISN mass gap. Under this framework, we perform binary population synthesis to explore the formation of GW190521-like systems via isolated binary evolution. We numerically calculate the binding energy parameter for massive stars at different metallicities, and employ them in our calculation for common envelope evolution. Our results prefer that the progenitor binaries formed in metal-poor environment with $\rm Z\leq0.0016$. The predicted merger rate density within redshift $z=1.1$ is $\sim 4\times 10^{-5}-5\times 10^{-2} \,\rm Gpc^{-3}yr^{-1}$. We expect that such events are potentially observable by upcoming both space and ground-based gravitational wave detectors.Comment: 20 pages, 9 figures, 8 tables. Accepted by MNRA

Thermomechanical Processing of Structural Steels with Dilute Niobium Additions

In this thesis, the influence of various dilute Nb additions (0.005–0.02 wt%) on austenite microstructure evolution along thermomechanical processing, in terms of austenite recrystallisation and grain growth, were investigated using steels with three carbon content levels. At the homogenisation temperature of 1250°C, all dilute Nb additions were dissolved in low carbon steels whereas the Nb dissolution limits for 0.4 wt% and 0.6 wt% C steels were 0.012 wt% and 0.08 wt%, respectively. Dilute Nb additions did not show significant influence on homogenised austenite grain size. The dramatic increase in C contents was more influential, which decreased the homogenised austenite grain size. The influence of dilute Nb additions and C contents on dynamic recrystallisation behaviour was studied by rough rolling at high deformation temperature with low strain rate. The dilute Nb additions were found to increase the critical strain of dynamic recrystallisation whereas C contents showed no influence on the dynamic recrystallisation behaviour. The austenite recrystallisation behaviour after finish rolling and after the holding period between finishing passes were studied by means of interrupted plane strain compression (PSC) tests and double hit PSC tests. It was found that with low Nb supersaturation, recrystallisation happened prior to Nb precipitation. Solute Nb in austenite delayed the onset of austenite recrystallisation through solute drag effect. With high Nb supersaturation, Nb precipitation occurred before the onset of recrystallisation which completely retarded austenite recrystallisation. The austenite grain growth during the holding period between finish rolling passes was studied by the evolution of prior-austenite grain size before and after the 20s holding period at the highest finish rolling temperature (1050°C). There was no Nb precipitation found and the difference in austenite grain growth behaviour was attributed to the solute drag effect from both Nb and C in solution. It was found that Nb in solution suppressed austenite grain growth. However, the effectiveness of solute Nb in suppressing austenite grain growth was affected by the C content

TOWARDS EFFICIENT PRESENTATION AND INTERACTION IN VISUAL DATA ANALYSIS

The "data explosion'' since the era of the Internet has increased data size tremendously, from several hundred Megabytes to millions of Terabytes. Large amounts of data may not fit into memory, and a proper way of handling and processing the data is necessary. Besides, analyses of such large scale data requires complex and time consuming algorithms. On the other hand, humans play an important role in steering and driving the data analysis, while there are often times when people have a hard time getting an overview of the data or knowing which analysis to run. Sometimes they may not even know where to start. There is a huge gap between the data and understanding. An intuitive way to facilitate data analysis is to visualize it. Visualization is understandable and illustrative, while using it to support fast and rapid data exploration of large scale datasets has been a challenge for a long time. In this dissertation, we aim to facilitate efficient visual data exploration of large scale datasets from two perspectives: efficiency and interaction. The former indicates how users could understand the data efficiently, this depends on various factors, such as how fast data is processed and how data is presented, while the latter focuses more on the users: how they deal with the data and why they interact with the system in a particular way. In order to improve the efficiency of data exploration, we have looked into two steps in the visualization pipeline: rendering and processing (computations). We first address visualization rendering of large dataset through a thorough evaluation of web-based visualization performance. We evaluate and understand the page loading effects of Scalable Vector Graphics (SVG), a popular image format for interactive visualization on the web browsers. To understand the scalability of individual elements in SVG based visualization, we conduct performance tests on different types of charts, in different phases of rendering process. From the results, we have figured out optimization techniques and guidelines to achieve better performance when rendering SVG visualization. Secondly, we present a pure browser based distributed computing framework (VisHive) that exploits computational power from co-located idle devices for visualization. The VisHive framework speeds up web-based visualization, which is originally designed for single computer and cannot make use of additional computational resources on the client side. It takes advantage of multiple devices that today's users often have access to. VisHive constructs visualization applications that can transparently connect multiple devices into an ad-hoc cluster for local computation. It requires no specific software to be downloaded for setup. To achieve a more interactive data analysis process, we first propose a proactive visual analytics system (DataSite) that enable users to analyze the data smoothly with a list of pre-defined algorithms. DataSite provides results through selecting and executing computations using automatic server-side computation. It utilizes computational resources exhaustively during data analysis to reduce the burden of human thinking. Analyzing results identified by these background processes are surfaced as status updates in a feed on the front-end, akin to posts in a social media feed. DataSite effectively turns data analysis into a conversation between the user and the computer, thereby reducing the cognitive load and domain knowledge requirements on users. Next we apply the concept of proactive data analysis to genomic data, and explore how to improve data analysis through adaptive computations in bioinformatics domain. We build Epiviz Feed, a web application that supports proactive visual and statistical analysis of genomic data. It addresses common and popular biological questions that may be asked by the analyst, and shortens the time of processing and analyzing the data with automatic computations. We further present a computational steering mechanism for visual analytics that prioritizes computations performed on the dataset leveraging the analyst's navigational behavior in the data. The web-based system, called Sherpa, provides computational modules for genomic data analysis, where independent algorithms calculate test statistics relevant to biological inferences about gene regulation in various tumor types and their corresponding normal tissues