Speaker
Description
Future e⁺e⁻ collider experiments place stringent performance demands on their detectors. As a foundation of particle physics measurements, the tracking system must not only reconstruct particle trajectories but also deliver high-precision momentum determination. A drift chamber equipped with cluster counting can significantly enhance particle identification; moreover, the recorded high-precision waveforms provide individual electron arrival times, offering the potential to surpass conventional position resolution and thereby substantially improve momentum resolution.
Conventional waveform processing in drift chambers typically records only the time at which the signal crosses a threshold and estimates the distance of closest approach (DOCA) via an x−t relation, making the measurement sensitive to ionization fluctuations and electron diffusion. In contrast, drift chambers utilizing cluster counting employ high-bandwidth, high-sampling-rate readout electronics that capture complete waveforms, making it possible, in principle, to extract the arrival time of each drift electron. While this approach opens the door to high-precision DOCA measurements, it also introduces significant complexities in inferring the DOCA from these arrival times, posing a significant challenge for reconstruction algorithms.
In this study, we develop a Transformer-based method with attention pooling to exploit the multiple electron arrival times extracted from the full waveform in the CEPC drift chamber. Simulation results demonstrate improvements of 27.2% in hit-position resolution and 45.0% in momentum resolution relative to conventional DOCA estimation using the x−t relation. This work lays the essential groundwork for high-precision waveform processing and track reconstruction at future colliders.
