AI Hunts for Hidden Physics in Particle Collisions, Finds Nothing—Yet
CERN researchers deployed machine learning to scan collision data for signs of undiscovered particles, analyzing a decade of experiments. The search turned up no anomalies, but the new detection method itself is a breakthrough that could accelerate future discoveries of exotic matter and inform next-generation detector designs.
Originaltitel: Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at √𝑠 =13 TeV with the ATLAS detector
<p>An anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets is presented. The search is based on 139 fb<sup>−1</sup> of proton-proton collisions at √𝑠 =13 TeVrecorded during 2015–2018 with the ATLAS detector at the Large Hadron Collider. The analysis is optimized without a particular signal model and aims to be sensitive to a broad range of new physics. It uses two different machine learning strategies to estimate the background in different signal regions. In each region, a weakly supervised classifier is trained to distinguish this background model from data. The analysis focuses on events with high transverse momentum jets reconstructed as large-radius jets. The mass and substructure of these jets are used as inputs to the classifiers. After a classifier-based selection, the distribution of the invariant mass of the two jets is used to search for potential local excesses. The model-independent results of both the anomaly detection methods show no signs of significant local excesses. In addition to model-independent results, a representative set of signal models is injected into the data, and the sensitivity of the methods to these scenarios is reported.</p>