Speaker
Description
Precise detector alignment is essential for reliable charged-particle tracking in high-energy physics experiments. Standard track-based alignment methods typically depend on iterative global fits which can become computationally demanding for large and highly segmented detectors.
We investigate a machine-learning approach in which detector sensors are represented as nodes in a graph, while reconstructed particle tracks define graph instances. The model is trained to use track-sensor correlations to identify detector elements that are likely to be misaligned, providing an alternative to conventional hierarchical alignment procedures.
In a proof-of-concept study, the method identifies single misaligned layers with an accuracy above 99% and shows encouraging behaviour in multi-layer misalignment scenarios. The approach also allows alignment quality to be scored at the sensor level, which could make it useful for detector performance monitoring and, potentially, near-real-time alignment diagnostics.