Search for Higgs Boson Production Beyond the Standard Model Using the Razor Kinematic Variables in pp Collisions at √s=8 TeV and Optimization of Higgs Boson Identification Using a Quantum Annealer

In the first part of this thesis we search for beyond the Standard Model physics through the search for anomalous production of the Higgs boson using the razor kinematic variables. We search for anomalous Higgs boson production using proton-proton collisions at center of mass energy √s=8 TeV collected by the Compact Muon Solenoid experiment at the Large Hadron Collider corresponding to an integrated luminosity of 19.8 fb^-1.

In the second part we present a novel method for using a quantum annealer to train a classifier to recognize events containing a Higgs boson decaying to two photons. We train that classifier using simulated proton-proton collisions at √s=8 TeV producing either a Standard Model Higgs boson decaying to two photons or a non-resonant Standard Model process that produces a two photon final state.

The production mechanisms of the Higgs boson are precisely predicted by the Standard Model based on its association with the mechanism of electroweak symmetry breaking. We measure the yield of Higgs bosons decaying to two photons in kinematic regions predicted to have very little contribution from a Standard Model Higgs boson and search for an excess of events, which would be evidence of either non-standard production or non-standard properties of the Higgs boson. We divide the events into disjoint categories based on kinematic properties and the presence of additional b-quarks produced in the collisions. In each of these disjoint categories, we use the razor kinematic variables to characterize events with topological configurations incompatible with typical configurations found from standard model production of the Higgs boson.

We observe an excess of events with di-photon invariant mass compatible with the Higgs boson mass and localized in a small region of the razor plane. We observe 5 events with a predicted background of 0.54 ± 0.28, which observation has a p-value of 10^-3 and a local significance of 3.35σ. This background prediction comes from 0.48 predicted non-resonant background events and 0.07 predicted SM higgs boson events. We proceed to investigate the properties of this excess, finding that it provides a very compelling peak in the di-photon invariant mass distribution and is physically separated in the razor plane from predicted background. Using another method of measuring the background and significance of the excess, we find a 2.5σ deviation from the Standard Model hypothesis over a broader range of the razor plane.

In the second part of the thesis we transform the problem of training a classifier to distinguish events with a Higgs boson decaying to two photons from events with other sources of photon pairs into the Hamiltonian of a spin system, the ground state of which is the best classifier. We then use a quantum annealer to find the ground state of this Hamiltonian and train the classifier. We find that we are able to do this successfully in less than 400 annealing runs for a problem of median difficulty at the largest problem size considered. The networks trained in this manner exhibit good classification performance, competitive with the more complicated machine learning techniques, and are highly resistant to overtraining. We also find that the nature of the training gives access to additional solutions that can be used to improve the classification performance by up to 1.2% in some regions.

Microwave-assisted synthesis and local analyses of positive insertion electrodes for Li-ion batteries

Efficient energy storage holds the key to reducing waste energy and enabling the use of advanced handheld electronic devices, hydrid electric vehicles and residential energy storage. Recently, Li-ion batteries have been identified and employed as energy storage devices due to their high gravimetric and volumetric energy densities, in comparison to previous technologies. However, more research is required to enhance the efficiency of Li-ion batteries by discovering electrodes with larger electrochemical discharge capacities, while maintaining electrochemical stability. The aims of this study are to develop new microwave-assisted synthesis routes to nanostructured insertion cathodes, which harbor a greater affinity for lithium extraction and insertion than bulk materials. Subsequent to this, state-of-the-art synchrotron based techniques have been employed to understand structural and dynamic behaviour of nanostructured cathode materials during battery cell operation. In this study, microwave-assisted routes to a-LiFePO4, VO2(B), V3O7, H2V3O8 and V4O6(OH)4 have all been developed. Muon spin relaxation has shown that the presence of b-LiFePO4 has a detrimental effect on the lithium diffusion properties of a-LiFePO4, in agreement with first principles calculations. For the first time, a-LiFePO4 nanostructures have been obtained by employing a deep eutectic solvent reaction media showing near theoretical capacity (162 mAh g–1). Studies on VO2(B) have shown that the discharge capacity obtained is linked to the synthesis method. Electrochemical studies of H2V3O8 nanowires have shown outstanding discharge capacities (323 mAh g–1 at 100 mA g–1) and rate capability (180 mAh g–1 at 1 A g–1). The electrochemcial properties of V4O6(OH)4 have been investigated for the first time and show a promising discharge capacity of (180 mAh g–1). Lastly, in situ X-ray absorption spectroscopy has been utilised to track the evolution of the oxidation states in a-LiFePO4, VO2(B) and H2V3O8, and has shown these can all be observed dynamically.