Speaker
Description
Large-scale point cloud and long-sequence processing are crucial for high energy physics applications such as pileup mitigation and track reconstruction. The HL-LHC presents inevitable challenges to machine learning models, requiring both high stability and low computational complexity. Previous studies have primarily focused on graph-based approaches which are generally effective but often struggle with computational complexity. In this study, we introduce the state space model with several key improvements. For example, based on similar logic as the Kalman Filter, Mamba is used with customized depth-wise convolution and SSM blocks. Ideally, Mamba should have inference times as short as Gated MLP when sequences become longer. We also have integrated a new matrix mixer and local-sensitive architecture into Mamba to further improve the throughput while having the same performance. To better simulate future realistic scenarios, we emphasize the long sequences case, where many models suffer from high complexity. Preliminary results show better performance than previous graph and transformer approach on node-level classification and clear physics evaluation metrics improvement on most kinematics regions yet still achieving a much stronger error-complexity/performance-speed tradeoff.
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