There has been a surge of interest in applying deep learning in particle and nuclear physics to replace labor-intensive offline data analysis with automated online machine learning tasks. This paper details a novel AI-enabled triggering solution for physics experiments in Relativistic Heavy Ion Collider and future Electron-Ion Collider. The triggering system consists of a comprehensive end-to-end pipeline based on Graph Neural Networks that classifies trigger events versus background events, makes online decisions to retain signal data, and enables efficient data acquisition. The triggering system first starts with the coordinates of pixel hits lit up by passing particles in the detector, applies three stages of event processing (hits clustering, track reconstruction, and trigger detection), and labels all processed events with the binary tag of trigger versus background events. By switching among different objective functions, we train the Graph Neural Networks in the pipeline to solve multiple tasks: the edge-level track reconstruction problem, the edge-level track adjacency matrix prediction, and the graph-level trigger detection problem. We propose a novel method to treat the events as track-graphs instead of hit-graphs. This method focuses on intertrack relations and is driven by underlying physics processing. As a result, it attains a solid performance (around 72% accuracy) for trigger detection and outperforms the baseline method using hit-graphs by 2% higher accuracy.

Abstract Scissor structures, which transform from a compact state to an expanded state, are widely used in various fields, ranging from architectural design to aerospace applications. We focus on a challenging problem that breaks through the restriction of planarity: to design scissor structures that expand from one given 3D shape to another 3D shape. To achieve this purpose, we propose a three‐step algorithm to construct a 3D scissor structure that realizes non‐uniform concentration between two different 3D curves. First, the input shapes are divided into scissor segments, which are composed of a sequence of planar scissor units based on the shape correspondence. Secondly, we compute the scissor unit geometry of each segment in a suggestive manner. Finally, the ball‐and‐socket joints with parameterized guide slits are integrated in order to connect the scissor segments, thereby completing the 3D deployment. Judging from a series of simulation and fabrication results, we demonstrate that our approach generates deployable structures for a wide range of 3D shape pairs.