Speaker
Description
PID is essential in high energy physics experiments, particularly for future e+e- colliders. The tera-z samples at FCC or CEPC offers vast flavor physics opportunities, necessitating robust hadron identification over a broad momentum range. Moreover, there is growing recognition of the performance gains that PID provides for jet flavor tagging. A key breakthrough in PID is cluster counting (dN/dx), which measures primary ionizations along a particle’s trajectory in gaseous detectors rather than relying on conventional dE/dx measurements, offering intrinsically superior resolution.
Technically, dN/dx can be implemented in both large-volume time projection chambers (TPCs) and drift chambers (DCHs), where cluster signals are projected to 2D positional readouts in the former and to 1D timing waveforms in the latter. Reconstruction is one of the major challenges of the dN/dx technique, as one must determine the number of primary cluster signals from an environment in which the signals are highly overlapped and contaminated by secondary ionization and noise.
Supervised learning is a statistical data-driven method capable of extracting complex information from large datasets. For TPCs, we have developed a point‑cloud algorithm using a GNN based on the Point Transformer architecture. Trained on extensive simulated samples, the model enhances pion–kaon separation by 10–20% compared to conventional truncated mean methods. For DCHs, we have developed a model based on LSTM and DGCNN that achieves a remarkable 10% improvement in pion-kaon separation on simulated samples related to traditional methods. Furthermore, for test-beam data samples collected at CERN--where label scarcity and data/MC discrepancies pose challenges--we have developed a semi-supervised domain adaptation model that outperforms traditional methods and maintains consistent performance across varying track lengths.
Three papers have been published related to this presentation: