Supersymmetry (SUSY) has been a topic of interest in the field of particle physics, offering potential solutions to existing unanswered questions. One intriguing aspect of SUSY is the prediction of “superpartner” particles for each known particle, such as the top squark for the top quark. Recent developments in the analysis of collision data at the Large Hadron Collider (LHC) have provided new insights into the search for these superpartners.
In 2021, the CMS collaboration revisited data collected between 2016 and 2018 to search for signs of stop particles, the supersymmetric partners of top quarks. The initial analysis indicated features that hinted at the presence of stop particles, prompting further investigation. Rather than waiting for additional data collection, the CMS collaboration decided to upgrade their analysis techniques to reexamine the existing dataset. This approach aimed to enhance the sensitivity of the analysis and reduce uncertainties associated with background simulations.
One of the primary challenges in analyzing stop particles is distinguishing their signal from background processes, particularly the production of top quarks with multiple jets. Traditional methods, such as the ABCD method, rely on uncorrelated observables to estimate background levels. However, in the case of stop searches, simple variables show correlations, rendering traditional methods ineffective. To address this issue, CMS physicists introduced innovative machine-learning techniques to identify two minimally correlated variables for data classification.
By leveraging advanced machine-learning techniques, the CMS collaboration successfully identified two variables with low correlation to partition the data into distinct regions. This novel approach allowed for accurate prediction of background levels without depending heavily on simulations. The analysis showcased a significant improvement in sensitivity, enabling physicists to rule out the presence of stop particles below a certain mass threshold. Despite not observing the signal in the reanalysis, the enhanced methodology sets the stage for future investigations during the ongoing LHC Run 3.
The absence of a signal in the updated analysis suggests constraints on specific SUSY scenarios, particularly regarding the mass of stop particles decaying into top quarks and jets. With a more sensitive analysis framework in place, researchers are optimistic about exploring new data from the current LHC operations. The refined techniques not only enhance the detection capabilities for SUSY particles but also pave the way for more profound insights into the fundamental workings of nature.
The advancements in supersymmetry analysis at the LHC reflect a concerted effort to refine detection methods and overcome analytical challenges. By incorporating machine-learning techniques and novel data classification approaches, physicists have made significant strides in improving the sensitivity of stop particle searches. The culmination of these efforts not only enhances our understanding of supersymmetry but also underscores the ongoing quest to unravel the mysteries of the universe through groundbreaking research at particle colliders.