Speaker
Description
Drift chambers are widely used for charged-particle tracking in nuclear and high-energy physics experiments. Track reconstruction commonly involves combinatorial hit association followed by fitting. In experiments, additional noise enlarges the search space, leading to increased and unstable tracking runtime, a problem for (near-) real-time analysis. Motivated by this behavior, neural-network-assisted hit filtering is investigated to suppress noise and limit combinatorial effects in drift chamber tracking. Hits are represented as nodes in an event-wise graph and classified as true or noise using a graph-based neural network prior to track fitting. Using simulated data consistent with experimental hit multiplicities, the proposed filter achieves high classification accuracy and reduces noise by approximately a factor of 100, limiting the number of hit combinations passed to the track fitting stage. These results suggest that neural-network-assisted hit filtering may serve as a useful pre-processing step for controlling tracking runtime. As neural-network inference introduces computational overhead, the overall runtime impact depends on the noise level and the balance between inference cost and noise reduction.
| Minioral | Yes |
|---|---|
| IEEE Member | No |
| Are you a student? | No |