Scientists Use Neural Networks to Analyze Large Hadron Collider Data, Discover Anomalies Pointing to Possible New Physics
In a groundbreaking move, scientists have utilized neural networks to analyze experimental data from the Large Hadron Collider’s ATLAS instrument for the first time. The analysis identified anomalies that warrant further investigation and may indicate the presence of new physics beyond the standard model.
Typically, particle physicists sift through vast amounts of data generated by particle colliders in search of potential evidence of new particles not accounted for in the Standard Model of particle physics. However, a team from the Argonne National Laboratory in the United States recently employed a new machine learning method to analyze a significant amount of ATLAS data, aiming to enhance efficiency and deviate from traditional approaches to identifying new physics.
Rather than seeking specific deviations from theoretical predictions, the goal of the new method is to pinpoint discrepancies that could signify previously unknown phenomena. Traditionally, scientists working with the ATLAS instrument relied on theoretical models to guide their experimental and analytical efforts, often employing complex computer simulations for comparison with real data.
Despite billions of collisions recorded by the ATLAS instrument thus far, no deviations from the Standard Model have been observed, and no new particles have been discovered since the detection of the Higgs boson in 2012. However, the Argonne National Laboratory team’s analysis of approximately 160 million events from 2015 to 2018 unearthed anomalies that demand further scrutiny. One such anomaly involves the decay of exotic particles with energies near 4.8 TeV, which produces muons and other particles in a manner inconsistent with Standard Model interactions.
This unexpected decay pattern suggests the potential existence of new and undiscovered particles, prompting the need for more extensive investigations. With rapid advancements in machine learning technology anticipated to contribute to the field of particle physics in the coming years, the team plans to apply this innovative approach to data collected during LHC Run-3.
As the scientific community eagerly awaits further developments in this area, the integration of neural networks and machine learning holds promise for uncovering the mysteries of the universe beyond what the current understanding dictates.