Fidalgo Rodríguez, Guillermo A.
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Publication Trigger studies for the Emerging Jets analysis and machine learning for Tracker DQM at CMS experiment(2024-05-08) Fidalgo Rodríguez, Guillermo A.; Malik, Sudhir; College of Arts and Sciences - Sciences; Marrero, Pablo; Santana-Colón, Samuel; Department of Physics; Sierra, HeidyThe Standard Model of Particle Physics (SM) has had a great track record over the decades. With the discovery of the top quark, the τ neutrino and the Higgs boson, the SM has proved it’s effectiveness and prediction prowess. Yet, it leaves behind open questions regarding problems like Dark Matter and thus a need for new physics. This has brought up many exotic searches in hopes of answering the questions that the SM has yet to address. To provide the necessary quality to search for new physics, physicists use the most complex machines ever designed. The preponderance of cosmological evidence suggests that the density dark matter energy density of the Universe is around 5 times the amount of regular baryonic matter, and hence, experimental searches have been developed to explain this. The CMS Collaboration has searched for signals of a dark matter model via the Emerging Jets analysis group. As with all experiments in High Energy physics, acquiring high quality of data is paramount to achieve groundbreaking science. The CMS experiment achieves the collection of it’s high quality data through the triggering and data acquisition systems put in place, but require manual labor to certify. In this work I present trigger efficiency studies relevant to the Emerging Jets analysis. Moreover, I present my work to improve the process of data certification in the DQM workflow implemented at the CMS Tracker DQM group. This work adds the automation of a new web application called the Machine Learning Playground designed to improve DQM shifter efficiency in data certification.