AI Learns to Read Particle Detector Signals More Accurately Than Human Methods
Physicists at the Large Hadron Collider have deployed a machine learning system that outperforms decades-old calibration techniques for analyzing particle collision data. The advance matters because it provides built-in confidence estimates for measurements, reducing hidden errors that could skew discoveries of new physics—and demonstrating how AI can solve measurement challenges across industries.
Originaltitel: Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network
<p>The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.</p>