Higher Order Couplings in the Clustering of Biased Tracers of Large-Scale Structure

The Large-Scale Structure (LSS) of the Universe, i.e. the distribution of matter and luminous tracers (such as galaxies), contains a wealth of information about the origin, composition, and evolution of the Universe. In order to extract this information, the non-linearities present in late-time observables provided by LSS surveys must be understood well. In general, there are three main sources of non-linearities: (1) non-linear matter clustering due to gravity; (2) non-linear biasing, i.e. the relation between the distribution of tracers and dark matter; and (3) primordial non-Gaussianity, which induces non-linearities in the initial conditions. The Effective Field Theory of Large-Scale Structure (EFTofLSS) provides a powerful framework to model the non-linear clustering due to gravity. In this thesis, we focus on understanding the non-linearities due to galaxy biasing using the EFTofLSS and numerical N-body simulations. This thesis is comprised of the following three projects: In the first part, we present a novel method to constrain quadratic and cubic galaxy bias parameters in dark matter simulations. The natural statistics to constrain quadratic and cubic bias parameters are tree-level bispectrum and trispectrum, respectively. Since these statistics are computationally quite expensive, we use efficient squared and cubic field estimators that contain integrated bispectrum and trispectrum information. We use the constraints to model the one-loop halo-matter power spectrum and show that the results agree with simulations up to kmax = 0.1h Mpc 1 once an additional derivative bias is implemented (Published in: Abidi & Baldauf, JCAP07(2018)029). In the second part, we develop a formalism to reconstruct the linear density field based on quadratic couplings in galaxy clustering. We employ a quadratic estimator inspired by Cosmic Microwave Background (CMB) lensing reconstruction. We incorporate non-linearities due to gravity, galaxy biasing and primordial non-Gaussianity, and verify our predictions with N-body simulations. We perform a Fisher matrix analysis on how the reconstructed field in combination with the biased tracer field can improve constraints on local type primordial non-Gaussianity. We find significant improvement on constraints due to cosmic variance cancellation resulting from the additional correlated modes of the reconstructed field, similar to multi-tracer analyses. In the third part, we develop a method to constrain non-linear galaxy bias parameters using the two- and three-point functions of projected galaxy clustering in correlation with CMB lensing convergence. The project thus aims to bring the methodology developed in project 1 above closer to data. We develop the quadratic field method for projected fields to avoid complications from non-linear redshift space distortions. We perform a Fisher forecast to show that this method can indeed be used to put constraints on bias parameters and the amplitude of matter fluctuations. Finally, using N-body simulations we ascertain that the projected statistics do indeed reduce the impact of finger-of-god corrections.My PhD was generously funded by the Cambridge Commonwealth, European and International Trust and the Higher Education Commission Pakistan. I have additionally received invaluable financial assistance from St. Edmunds College, Cambridge, the Cambridge Philosophical Society, the Centre for Theoretical Cosmology, Dr Blake Sherwin's EPRC grant, the Postgraduate Lundgren Award, and the Santander Award

Referenceable mobile crowdsensing architecture: A healthcare use case

Smartphones have become an integral part in life of users, mainly because over the course of recent years, they have become extremely mainstream, cheap, flexible, and they pack high-end hardware that offers high computational capabilities. Many, if not all of today’s smartphones are equipped with sophisticated sensors which enable smart mobile sensing. The programmable nature of these sensors in the smartphones enable a wide array of possibilities to achieve user-centric or environmental sensing. Even though there have been different approaches proposed to develop a smartphone app, platform, design frameworks, APIs, and even application-specific architectures, there is a lack of generalised referenceable architecture in the literature. In this paper, we propose a generic reference architecture, which can be derived to create more concrete mobile sensing or mobile app architectures. Furthermore, we realise the proposed reference architecture in a healthcare use case, specifically in the context of applying smart mobile sensing to support tinnitus research

Towards Automated Smart Mobile Crowdsensing for Tinnitus Research

Tinnitus is a disorder that is not entirely understood, and many of its correlations are still unknown. On the other hand, smartphones became ubiquitous. Their modern versions provide high computational capabilities, reasonable battery size, and a bunch of embedded high-quality sensors, combined with an accepted user interface and an application ecosystem. For tinnitus, as for many other health problems, there are a number of apps trying to help patients, therapists, and researchers to get insights into personal characteristics but also into scientific correlations as such. In this paper, we present the first approach to an app in this context, called TinnituSense that does automatic sensing of related characteristics and enables correlations to the current condition of the patient by a combined participatory sensing, e.g., a questionnaire. For tinnitus, there is a strong hypothesis that weather conditions have some influence. Our proof-of-concept implementation records weather-related sensor data and correlates them to the standard Tinnitus Handicap Inventory (THI) questionnaire. Thus, TinnituSense enables therapists and researchers to collect evidence for unknown facts, as this is the first opportunity to correlate weather to patient conditions on a larger scale. Our concept as such is limited neither to tinnitus nor to built-in sensors, e.g., in the tinnitus domain, we are experimenting with mobile EEG sensors. TinnituSense is faced with several challenges of which we already solved principle architecture, sensor management, and energy consumption

Towards Incentive Management Mechanisms in the Context of Crowdsensing Technologies based on TrackYourTinnitus Insights

The increased use of mobile devices has led to an improvement in the public health care through participatory interventions. For example, patients were empowered to contribute in treatment processes with the help of mobile crowdsourcing and crowdsensing technologies. However, when using the latter technologies, one prominent challenge constitutes a continuous user engagement. Incentive management techniques can help to tackle this challenge by motivating users through rewards and recognition in exchange of task completion. For this purpose, we aim at developing a conceptual framework that can be integrated with existing mHealth mobile crowdsourcing and crowdsensing platforms. The development of this framework is based on insights we obtained from the TrackYourTinnitus (TYT) mobile crowdsensing platform. TYT, in turn, pursues the goal to reveal insights to the moment-to-moment variability of patients suffering from tinnitus. The work at hands presents evaluated data of TYT and illustrates how the results drive the idea of a conceptual framework for an incentive management in this context. Our results indicate that a proper incentive management should play an important role in the context of any mHealth platform that incorporates the idea of the crowd

Computational Model Development for Hybrid Tilting Pad Journal Bearings Lubricated with Supercritical Carbon Dioxide

Fluid film bearings lubricated with supercritical carbon dioxide (sCO2) eliminate the infrastructural requirement for oil lubricant supply and sealing in turbomachinery for sCO2 power systems. However, sCO2’s thermohydrodynamic properties, which depend on pressure and temperature, pose a challenge, particularly with computational model development for such bearings. This study develops a computational model for analyzing sCO2-lubricated tilting pad journal bearings (TPJBs) with external pressurization. Treating sCO2 as a real gas, the Reynolds equation for compressible turbulent flows solves the pressure distribution using the finite element method, and the Newton−Raphson method determines the static equilibrium position by simultaneously calculating forces, moments, flow rates of externally pressurized sCO2, and pressure drop due to flow inertia. The finite difference method solves the energy equation for temperature distribution. The density and viscosity of sCO2 are converged using the successive substitution method. The obtained predictions agree with the previous and authors’ computational fluid dynamics predictions, thus validating the developed model. Hybrid lubrication increases the minimum film thickness and stiffness up to 80% and 65%, respectively, and decreases the eccentricity ratio by up to 65% compared to those of pure hydrodynamic TPJB, indicating significant improvement in the load capacity. The bearing performance is further improved with increasing sCO2 supply pressure

A new approach for quantification of corrosion losses on steels exposed to an artificial seawater environment

The selection methodology for thickness loss measurement is very important to determine the extent of corrosion damage, as well as in formulation of corrosion prediction models and inspection/maintenance plans for offshore structures. This paper introduces a more accurate corrosion measurement technique, based on the pre-exposure dimensional metrology and post-exposure optical microscopy/image analysis on the cross-sections of steel samples. During this corrosion test, the surface grinded and uncoated steel samples were submerged vertically in an artificial seawater solution, for a duration of up to a maximum of 365 days. The corrosion damage experienced on the steel samples means that the dimensional metrology can be more accurate, and useful approach to measure both uniform and localised corrosion losses simultaneously than the conventional average mass loss method