Browsing by Subject "automation"

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.