Speaker
Description
Charge particle track reconstruction is the foundation of the collider experiments. Yet, it's also the most computationally expensive part of the particle reconstruction. The innovation in tracking reconstruction using graph neural networks (GNNs) has demonstrated a promising capability to address the computing challenges posed by the High-Luminosity LHC (HL-LHC) with Machine learning. However, GNNs face limitations involving irregular computations and random memory access, slowing down their speed. In this talk, we introduce a Locality-Sensitive Hashing-Based Efficient Point Transformer (HEPT) with advanced attention methods, offering a superior alternative with near-linear complexity, achieving milliseconds of latency and memory consumption. We present a comprehensive evaluation of HEPT's computational efficiency and physics performance compared to other algorithms, such as GNN-based pipelines, highlighting its potential to accelerate full track reconstruction with novel object condensation and track building approaches.
