A Search For New Low-Mass Diphoton Resonances At Atlas And An Investigation Into Using Gaussian Process Regression To Model Non-Resonant Two-Photon Standard Model Backgrounds

The Standard Model of particle physics has been tested over many years with many ex- periments and has predicted experimental results with remarkable accuracy. In 2012, the last piece of the Standard Model, the Higgs boson, was discovered by the experiments ATLAS and CMS at the Large Hadron Collider (LHC). Although this completes the Standard Model, this by no means completes our picture of the physics that describes the observable universe. Several phenomena and measurements remain unexplained by the Standard Model including gravity, dark matter, the baryon-antibaryon asymmetry of the universe and more. One of the primary goals of the LHC and the ATLAS experiment are to search for extensions and modifications to the Standard Model that could help to explain these phenomena. This the- sis presents three areas where I made major contributions. The first is in the identification of prompt electrons in ATLAS using a likelihood method both in the online trigger system and in offline data analysis. Prompt electrons are ubiquitous in the signatures of electroweak physics, one of the cornerstones of the ATLAS physics program. Next I present a search for new physics in low-mass (65-110 GeV) diphoton events. This is a model independent search that is motivated by several extensions to the Standard Model including the two Higgs doublet model where new scalars can appear as lighter versions of the Standard Model Higgs. No evidence for a new narrow resonance is found, so limits ranging from 30 to 101 fb are set on the production cross section of such a resonance, assuming that its branching fraction to two photons is 100 percent. The sensitivity of these results are limited by the systematic uncertainties due to the potential spurious signals introduced by the two-photon non-resonant Standard Model background. My third contribution was an initial investigation of a new method to model this background using Gaussian Process Regression

Comparison of Fuel Consumption and Fuel Cell Degradation Using an Optimised Controller

The Energy Management Strategy (EMS) of any hybrid vehicle is responsible for determining the operating state of many components on board the vehicle and therefore has significant effect on the fuel economy, emissions, ageing of components and vehicle drive-ability. It is generally accepted that Stochastic Dynamic Programming (SDP) can be used to produce a near-optimal control strategy provided that an accurate Markov model of the drive-cycle is available, and the cost function used for the optimisation is representative of the true running cost of the vehicle. The vast majority of research in this field focussing solely on the optimisation of the fuel economy, however for a fuel cell hybrid vehicle, the degradation of the fuel cell contributes significantly to the overall running cost of the vehicle, and should therefore be included in calculation of the running cost during the optimisation process. In this work, an optimised controller using SDP is developed for a campus passenger vehicle in order to minimise the lifetime cost of both fuel consumption and fuel cell degradation. The vehicle is then simulated over a number of typical journeys obtained from data logging during its use on the University of Birmingham's campus. It is shown that the expected lifetime cost due to fuel cell degradation massively outweighs the cost of the fuel consumed

An energy management strategy to concurrently optimise fuel consumption & PEM fuel cell lifetime in a hybrid vehicle

The cost and reliability of fuel cells are major obstructions preventing fuel cell hybrid electric vehicle (FCHEV) from entering the mainstream market. However, many of the degradation methods are strongly affected by the operating conditions of the fuel cell and therefore can be mitigated by optimisation of the Energy Management Strategy (EMS). The major causes of fuel cell degradation are identified from the literature and a model is produced in order to estimate the effect of the EMS on the fuel cell degradation. This is used to produce an optimal strategy for a low speed campus vehicle using Stochastic Dynamic Programming (SDP). The SDP controller attempts to minimise the total running cost of the fuel cell, inclusive of both fuel consumption and degradation, each weighted by their respective costs. The new strategy is shown to increase the lifetime of the fuel cell by 14%, with only a 3.5% increase in fuel consumption, largely by avoiding transient loading on the fuel cell stack

Modelling of distributed time constants in carbon based supercapacitors

Modelling of distributed time constants in carbon based supercapacitor

The modelling of carbon-based supercapacitors: distributions of time constants and Pascal Equivalent Circuits

Supercapacitors are an emerging technology with applications in pulse power, motive power, and energy storage. However, their carbon electrodes show a variety of non-ideal behaviours that have so far eluded explanation. These include Voltage Decay after charging, Voltage Rebound after discharging, and Dispersed Kinetics at long times. In the present work, we establish that a vertical ladder network of RC components can reproduce all these puzzling phenomena. Both software and hardware realizations of the network are described. In general, porous carbon electrodes contain random distributions of resistance R and capacitance C, with a wider spread of log R values than log C values. To understand what this implies, a simplified model is developed in which log R is treated as a Gaussian random variable while log C is treated as a constant. From this model, a new family of equivalent circuits is developed in which the continuous distribution of log R values is replaced by a discrete set of log R values drawn from a geometric series. We call these Pascal Equivalent Circuits. Their behaviour is shown to resemble closely that of real supercapacitors. The results confirm that distributions of RC time constants dominate the behaviour of real supercapacitors

Ternary mixtures of sulfolanes and ionic liquids for use in high-temperature supercapacitors

Ionic liquids are a natural choice for supercapacitor electrolytes. However, their cost is currently high. In the present work, we report the use of ternary mixtures of sulfolane, 3-methyl sulfolane, and quaternary ammonium salts (quats) as low-cost alternatives. Sulfolane was chosen because it has a high Hildebrand solubility parameter (δ H = 27.2 MPa 1/2 ) and an exceptionally high dipole moment (μ = 4.7 D), which means that it mixes readily with ionic liquids. It also has a high flash point (165 °C), a high boiling point (285 °C), and a wide two-electrode (full-cell) voltage stability window ( > 7 V). The only problem is its high freezing point (27 °C). However, by using a eutectic mixture of sulfolane with 3-methyl sulfolane, we could depress the freezing point to -17 °C. A second goal of the present work was to increase the electrical conductivity of the electrolyte beyond its present-day value of 2.1 mS cm -1 at 25 °C, currently provided by butyltrimethylammonium bis(trifluoromethylsulfonyl)imide (BTM-TFSI). We explored two methods of doing this: (1) mixing the ionic liquid with the sulfolane eutectic and (2) replacing the low-mobility TFSI anion with the high-mobility MTC anion (methanetricarbonitrile). At the optimum composition, the conductivity reached 12.2 mS cm -1 at 25 °C

Structure-transport relationships in disordered solids using integrated rate of gas sorption and mercury porosimetry

This work describes a new experimental approach that delivers novel information on structure-transport relationships in disordered porous pellets. Integrated rate of adsorption and mercury porosimetry experiments have been used to probe the relative importance of particular sub-sets of pores to mass transport rates within the network of two disordered porous solids. This was achieved by examining the relative rates of low pressure gas uptake into a network, both before, and after, a known set of pores was filled with frozen, entrapped mercury. For catalyst pellets, formed by tableting, it has been found that the compaction pressure affects the relative contribution to overall mass transport made by the subset of the largest pores. Computerised X-ray tomography (CXT) has been used to map the spatial distribution of entrapped mercury and revealed that the relative importance of the sub-sets of pores is related to their level of pervasiveness across the pellet, and whether they percolate to the centre of the pellet. It has been shown that a combination of integrated mercury porosimetry and gas sorption, together with CXT, can comprehensively reveal the impact of manufacturing process parameters on pellet structure and mass transport properties. Hence, the new method can be used in the design and optimisation of pellet manufacturing processes