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Using contemporary data science methods to search for dark matter at the LHC
Olga Sunneborn Gudnadottir
(PhD starting seminar)
Despite the great success of the Standard Model in describing elementary particles and their interactions, a lot of questions in this domain still go unanswered, such as “What is Dark Matter?”, “Why are there three generations of particles?” and “Why is there more matter than anti-matter in the universe?”. This tells us that there is new physics to be found, which is the main goal of the experiments at the Large Hadron Collider (LHC) at CERN.
The LHC has been running successfully for 10 years, giving us the Higgs boson discovery in 2012 and precision measurements. No signs of new physics have been observed, however, and advances in both hardware and software are sought after. To address the challenges of finding potentially small new physics signals within the very large data samples of the LHC, data science and machine learning methods are already prominent at the LHC; and will only become more important as we move to the High-Luminosity LHC (HL-LHC). The HL-LHC, a major upgrade of the LHC, is planned to be ready in 2026 and will deliver datasets one order of magnitude larger.
The subject of my PhD is to search for Dark Matter in the data collected by the ATLAS experiment of the LHC, using data science methods that I will develop myself. I am currently working on an analysis targeting Stealth Dark Matter in ATLAS, which I will present in this seminar. I will also talk about the current state of data science methods in High Energy Physics and my plans for developing and applying my own.
Finally, I will briefly discuss the upgrade of of the hardware track trigger planned for ATLAS in the HL-LHC, in which I will perform my service work for the ATLAS collaboration.