Shuzhe Li | Searcharxiv

arXiv2026

Fast Self-Triggering and Segment-Fitting of Cosmic Muons with a small-Diameter Monitored Drift Tube Detector

We present a fast self-triggering and segment-fitting algorithm that identifies cosmic muons and reconstructs their directions using a small-diameter Monitored Drift Tube (sMDT) chamber without an external trigger detector. The muon arrival time is determined using a Legendre-based algorithm combined with coarse- and fine-grid searches. We implement the algorithm in FPGA firmware on an ATLAS MDT miniDAQ board and evaluate its efficiency, resolution, and latency. The results demonstrate standalone sMDT triggering and trigger-level reconstruction of muon directions, providing a solution for chamber commissioning and potentially MDT-based triggers for charged massive stable particle searches at the High Luminosity-LHC.

physics.ins-det↗

arXiv2022

Mutual Distillation Learning Network for Trajectory-User Linking

Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextual features in check-in data, resulting in poor performance for TUL task. In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL. Specifically, MainTUL is composed of a Recurrent Neural Network (RNN) trajectory encoder that models sequential patterns of input trajectory and a temporal-aware Transformer trajectory encoder that captures long-term time dependencies for the corresponding augmented historical trajectories. Then, the knowledge learned on historical trajectories is transferred between the two trajectory encoders to guide the learning of both encoders to achieve mutual distillation of information. Experimental results on two real-world check-in mobility datasets demonstrate the superiority of MainTUL against state-of-the-art baselines. The source code of our model is available at https://github.com/Onedean/MainTUL.

cs.LG↗