Browsing by study line "Particle Physics and Cosmology"

The measurement of quantum states has been a widely studied problem ever since the discovery of quantum mechanics. In general, we can only measure a quantum state once as the measurement itself alters the state and, consequently, we lose information about the original state of the system in the process. Furthermore, this single measurement cannot uncover every detail about the system's state and thus, we get only a limited description of the system. However, there are physical processes, e.g., a quantum circuit, which can be expected to create the same state over and over again. This allows us to measure multiple identical copies of the same system in order to gain a fuller characterization of the state. This process of diagnosing a quantum state through measurements is known as quantum state tomography. However, even if we are able to create identical copies of the same system, it is often preferable to keep the number of needed copies as low as possible. In this thesis, we will propose a method of optimising the measurements in this regard. The full description of the state requires determining multiple different observables of the system. These observables can be measured from the same copy of the system only if they commute with each other. As the commutation relation is not transitive, it is often quite complicated to find the best way to match the observables with each other according to these commutation relations. This can be quite handily illustrated with graphs. Moreover, the best way to divide the observables into commuting sets can then be reduced to a well-known graph theoretical problem called graph colouring. Measuring the observables with acceptable accuracy also requires measuring each observable multiple times. This information can also be included in the graph colouring approach by using a generalisation called multicolouring. Our results show that this multicolouring approach can offer significant improvements in the number of needed copies when compared to some other known methods.

Strongly coupled matter called quark–gluon plasma (QGP) is formed in heavy-ion collisions at RHIC [1, 2] and the LHC [3, 4]. The expansion of this matter, caused by pressure gradients, is known to be hydrodynamic expansion. The computations show that the expanding QGP has a small shear viscosity to entropy density ratio (η/s), close to the known lower bound 1/4π [5]. In such a medium one expects that jets passing through the medium would create Mach cones. Many experimental trials have been done but no proper evidence of the Mach cone has been found [6, 7]. Mach cones were thought to cause double-bumps in azimuthal correlations. However these were later shown to be the third flow harmonic. In this thesis a new method is proposed for finding the Mach cone with so called event-engineering. The higher order flow harmonics and their linear response are known to be sensitive to the medium properties [8]. Hence a Mach cone produced by high momentum jet would change the system properties and, thus, the observable yields. Different flow observables are then studied by selecting high energy jet events with different momentum ranges. Considered observables for different momenta are then compared to the all events. Found differences in the flow harmonics and their correlations for different jet momenta are reported showing evidence of Mach cone formation in the heavy-ion collisions. The observations for different jet momenta are then quantified with χ 2 -test to see the sensitivities of different observables to momentum selections.

At the Compact Muon Solenoid (CMS) experiment at CERN (European Organization for Nuclear Research), the building blocks of the Universe are investigated by analysing the observed final-state particles resulting from high-energy proton-proton collisions. However, direct detection of final-state quarks and gluons is not possible due to a phenomenon known as colour confinement. Instead, event properties with a close correspondence with their distributions are studied. These event properties are known as jets. Jets are central to particle physics analysis and our understanding of them, and hence of our Universe, is dependent upon our ability to accurately measure their energy. Unfortunately, current detector technology is imprecise, necessitating downstream correction of measurement discrepancies. To achieve this, the CMS experiment employs a sequential multi-step jet calibration process. The process is performed several times per year, and more often during periods of data collection. Automating the jet calibration would increase the efficiency of the CMS experiment. By automating the code execution, the workflow could be performed independently of the analyst. This in turn, would speed up the analysis and reduce analyst workload. In addition, automation facilitates higher levels of reproducibility. In this thesis, a novel method for automating the derivation of jet energy corrections from simulation is presented. To achieve automation, the methodology utilises declarative programming. The analyst is simply required to express what should be executed, and no longer needs to determine how to execute it. To successfully automate the computation of jet energy corrections, it is necessary to capture detailed information concerning both the computational steps and the computational environment. The former is achieved with a computational workflow, and the latter using container technology. This allows a portable and scalable workflow to be achieved, which is easy to maintain and compare to previous runs. The results of this thesis strongly suggest that capturing complex experimental particle physics analyses with declarative workflow languages is both achievable and advantageous. The productivity of the analyst was improved, and reproducibility facilitated. However, the method is not without its challenges. Declarative programming requires the analyst to think differently about the problem at hand. As a result there are some sociological challenges to methodological uptake. However, once the extensive benefits are understood, we anticipate widespread adoption of this approach.

The high luminosity upgrade of the Large Hadron Collider (LHC) will result in higher collision rates and current equipment is not up to par with this future era of operations. Identification and reconstruction of hard interactions may be hampered by the spatial overlapping of particle tracks and energy deposits from additional collisions and this often leads to false triggers. In addition, current particle detectors suffer from radiation damage that severely affects the accuracy of our results. The new minimum ionizing particle (MIP) timing detector will be equipped with low gain avalanche detectors which have a comparably small timing resolution that helps with the track reconstruction and their thin design limits the radiation damage over time. In this thesis, we build an experimental set-up in order to study the timing resolution of these detectors closely. In order to find the timing resolution, we take the time difference between the signals from two detectors and put it in a histogram to which we apply a Gaussian fit. The standard deviation of this Gaussian is called the time spread from which we can estimate the timing resolution. We first build, characterize and improve our experimental set-up using reference samples with known timing resolution until our set-up is able to reproduce the reference value. Then we repeat the measurements with irradiated samples in order to study how radiation damage impacts timing. We were able to adjust our setup with reference samples until we measured a timing resolution of 33$\pm$2~ps. We use this result to calculate the timing resolution of an irradiated sample ($8.0 \times 10^{14}$ proton fluence) and we found a timing resolution of 62$\pm$2~ps. This thesis also discusses the analysis of the data and how the data can be re-analyzed to try to improve the final result.