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I am working on the search for four top quark production in CMS Experiment. Four top quark production is a rare particle process where two beams of protons collide inside the LHC and gives out four top quarks. To be a bit more precise, they are a pair of top-antitop quarks, or two top quarks and two anti-top quarks. This particle production is dictated by the Standard Model, which is our current model of fundamental particles. If we can find it in the LHC and measure how often this occurs inside the CMS detector, we can determine if the Standard Model is corrector not. If it is correct, then that’s great. If it is not, that is great too, because this means the Standard Model has a flaw and we need better theories that can fill in the gap from our friends working in theoretical physics.

On the other hand, I also have a lot of interest on Machine Learning, so that’s why I decided to work on Machine Learning applications for CMS data quality monitoring. CMS detector is a particle detector that can detect particles generated from collisions from the LHC, but just like a camera on your phone, sometimes it just malfunctions and some components may not work. We need to be able to know if any component has gone malfunctioned, and we need to know it fast. Currently we need fellow physicists that would watch closely at the detector and let responsible people know if there’s a problem. With the upcoming upgrades for the LHC in the future, we will have a huge amount of data coming out of the detector, and we need something that can handle a deluge of data when these upgrades are now in place. That is why we turn to Machine Learning as a tool to help alert us when the detector has problems, so we can intervene quickly and fix it.