An extension of Associative Memory approach to tracking with a drift-tube detector using timing information and its demonstration for HL-LHC ATLAS muon trigger

High-speed online pattern recognition has been an important challenge for triggering in High Energy Physics (HEP) experiments. The Associative Memory (AM) approach has been developed and used in the HEP experiments for online track-finding with silicon detectors. We intend to extend the AM approach to tracking with a drift-tube detector, using the drift-time as a “new dimension” of observables in addition to spatial information. Our benchmark study demonstrates the feasibility of the extended AM concept, aiming at the online muon reconstruction with ATLAS Monitored Drift-Tube (MDT) detector for Phase-2 Level-0 muon trigger system. The online muon reconstruction will consist of two parts; (1) fast track segment finding, and (2) momentum reconstruction. We point out that timing information can be integrated into the AM approach in a natural way, and the AM approach can fit the needs for the fast track segment finding. In terms of hardware specifications expected for the Phase-2 Level-0 muon trigger system, an optimal pattern training scheme is developed to prepare an effective set of AM patterns that provide high efficiency in the track finding while keeping a good resolution. Based on the system-level design of electronics, an optimal algorithm chain has been developed to minimize the latency for the track segment finding. The detailed design and performance study shows the AM approach has the capability of a high-speed and high-performance track-finding with drift-tube detectors.