CERN’s ATLAS Searches for SUSY Particles Using LHC Data

The ATLAS Collaboration at the Large Hadron Collider has intensified the hunt for supersymmetric (SUSY) particles, employing machine-learning techniques to push the boundaries of particle detection and establish the strongest constraints yet on their properties. Theorists propose that SUSY, which posits a “superpartner” for each particle in the Standard Model, could explain mysteries like the unexpectedly small mass of the Higgs boson and the composition of dark matter; the lightest neutralino is a leading dark matter candidate. Researchers analyzed data from the LHC’s second run, collected between 2015 and 2018, focusing on subtle signals from decaying particles, including a “disappearing track” left by a chargino. By deploying machine-learning techniques, the ATLAS Collaboration has been able to significantly improve the experiment’s sensitivity to low-energy particles, and while no SUSY particles were observed, these new results supersede previous limitations set by the Large Electron, Positron Collider, refining the direction of future searches. ATLAS Collaboration’s Machine Learning Improves Low-Energy Particle Sensitivity The ATLAS Collaboration has achieved a significant leap in particle detection sensitivity, pushing the boundaries of what’s possible in the search for supersymmetry. Utilizing machine-learning techniques, physicists have enhanced their ability to identify extremely low-energy particles, crucial for detecting potential supersymmetric (SUSY) particles predicted by theoretical models seeking to explain phenomena like the Higgs boson’s mass and the composition of dark matter. According to the theory of supersymmetry, every particle in the Standard Model possesses a “superpartner,” with the higgsino being a key focus of these investigations, though its detection is complicated by its likely manifestation as a mixture of neutralinos and charginos. The challenge lies in the expected low-energy signatures of these decaying particles, making them exceptionally difficult to isolate amidst the noise of proton-proton collisions; however, the ATLAS Collaboration deployed neural networks to meticulously examine the low-momentum regions of pions and leptons, seeking evidence of SUSY particle decay. These analyses focused on data collected during the LHC’s second run, spanning 2015 to 2018, and included a search for “disappearing tracks” left by charginos decaying into invisible neutralinos and low-energy pions. Detecting these particles presents a significant challenge, as decays are predicted to yield minimal energy and produce low-energy particles difficult to distinguish from background noise in proton-proton collisions. Another search examined heavier neutralinos decaying into the lightest neutralino and low-momentum leptons, employing neural networks to sift through the data, and this approach allows researchers to better isolate potential signals. Source: https://home.cern/news/news/physics/atlas-sets-strong-limits-supersymmetry ATLAS COLLABORATION DARK MATTER HIGGS BOSON MACHINE LEARNING SUPERSYMMETRY