Supersymmetry, also known as SUSY, is a theory in particle physics that proposes the existence of “superpartner” particles for each known particle. This theory aims to address some of the unanswered questions in the field of particle physics by introducing new particles with unique properties.
In a recent study conducted by the CMS collaboration, data collected from 2016 to 2018 was reanalyzed to search for signs of stop particles, the superpartners of top quarks. The initial analysis hinted at the presence of stop particles, but to confirm this, the researchers upgraded their analysis techniques to increase the accuracy of their findings.
One of the primary challenges in this analysis is differentiating between the signal produced by stop particles and the background noise from the pair production of top quarks, a common process at the Large Hadron Collider (LHC). This is complicated by the fact that both processes result in similar particle clusters known as “jets.”
To overcome the limitations of traditional methods like the ABCD method, the CMS physicists employed advanced machine-learning techniques to identify two variables with minimal correlation. By utilizing these variables to divide the data into distinct regions, the researchers were able to accurately predict the background noise without relying solely on simulations.
By implementing this novel approach, the CMS collaboration significantly increased the sensitivity of their analysis, leading to a more precise understanding of the data. While the expected signal of stop particles was not observed in this analysis, the results suggest that in certain SUSY scenarios, stop particles with masses greater than 700 GeV must exist.
With a more sensitive analysis method now in place, physicists are optimistic about delving deeper into the data from the ongoing LHC Run 3. This ongoing quest for supersymmetry promises to uncover new insights into the fundamental nature of particles and potentially reveal where the secrets of the universe lie hidden.
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