Speaker
Description
During recent Belle II data-taking, localized efficiency losses were observed in the Central Drift Chamber (CDC), concentrated in specific $\phi$ regions. These losses originated from consecutive front-end board failures that disabled an entire CDC superlayer. A superlayer consists of group of consecutive concentric axial or stereo wire layers essential for three-dimensional tracking. The resulting inefficiency reduced hit coverage and caused the multivariate (MVA) track quality estimator to reject tracks crossing the affected region, as it had not been trained with this adverse scenario.
Nevertheless, the existing CDC Data Quality Monitoring (DQM) framework did not expose this problem in real time. Wire health was monitored only via hit counts and time distributions, which remained within normal ranges. Also the overall tracking efficiency appeared stable because lost CDC tracks were frequently recovered by the Silicon Vertex Detector (SVD). The underlying cause of the efficiency loss was identified only after the introduction of new geometrically accurate wire-efficiency plots. These plots provide a wire-by-wire measure of the probability of hit attachment to reconstructed tracks, enabling operators to diagnose detector problems during data acquisition. In addition, wires are classified into high-, medium-, or low-efficiency categories, which serve as practical indicators for analysts when selecting data for physics analyses. Run-by-run summaries of these parameters are now available within the Belle II monitoring framework.
This new monitoring approach closes a critical gap in the CDC DQM by linking wire health directly to tracking efficiency, thereby supporting both real-time detector operation and offline data quality assessment. It will be essential not only for detecting hardware-related failures but also for studying long-term effects such as the continuous gain drop observed in CDC wires during continuous run period, helping in understanding and mitigating aging effects in gas detectors. The information gained has also motivated efforts to retrain the MVA-based track quality filters to include such failure modes, which is crucial since the estimator plays a key role in suppressing fake tracks and stabilizing High-Level Trigger (HLT) performance.
| Position | Engineer |
|---|---|
| Affiliation | SSS Defence, Plot No 283B, Bommasandra Jigani Link Road, K.I.A.D.B Industrial Area Bengaluru, Karnataka 560105 |
| Country | India |
