Trajectory Reconstruction

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9 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Track finding and particle identification performance with particle physics foundation models
Oct 22, 2026Shuhang Li, Haiwang Yu, Gabor Galgoczi +9Trajectory Reconstruction High-Energy Physics
Columbia University, New York, NY 10027, USA · Brookhaven National Laboratory, Upton, NY 11973, USA · State University of New York, Stony Brook, New York, NY 11794, USA +1
Semantic Edge-Cloud Communication for Real-Time Urban Traffic Digital Twins
Oct 9, 2026Murat Arda Onsu, Arman Dogru, Poonam Lohan +2Trajectory Reconstruction Intelligent Transportation Systems
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada. · Nokia Bell Labs, 600 March Road, Kanata, ON K2K 2E6, Canada.
Temporal Gradient Inversion for Private Trajectory Reconstruction in Embodied Reinforcement Learning
Sep 24, 2026Sudip Bhujel, Shanghao Shi, Ruiquan Huang +2Gradient Inversion Attacks Trajectory Reconstruction
Department of Computer Science, University of Kentucky · Department of Computer Science and Engineering, Washington University in St. Louis
REFINE: Trajectory Representation Learning via Closed-Loop Transcription -- Extended Version
Sep 7, 2026Sean Bin Yang, Ying Sun, Jilin Hu +5Contrastive Learning Trajectory Reconstruction
Aalborg University Aalborg, Denmark · Chongqing Universtiy of Posts and Telecommunications Chongqing, China · East China Normal University Shanghai, China +1
TRACER: Training-Free Closed-Loop Structured Inference for Traffic Accident Reconstruction
Jun 23, 2026Yanchen Guan, Chengyue Wang, Bin Rao +5Trajectory Reconstruction
State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR, 999078, China
Metric-Aware Hybrid Forecasting for the CTF4Science Lorenz Challenge
Jun 2, 2026Cen LuTrajectory Reconstruction Time Series Forecasting
EPFL & Idiap Research Institute · Switzerland
O-ConNet: Geometry-Aware End-to-End Inference of Over-Constrained Spatial Mechanisms
Apr 2, 2026Haoyu Sun, Meng Zhao, Tianhao Wang +1Trajectory Reconstruction Parameter Estimation
School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China.
TRACE: Trajectory Recovery with State Propagation Diffusion for Urban Mobility
Mar 19, 2026Jinming Wang, Hai Wang, Hongkai Wen +2Trajectory Reconstruction Diffusion Models
University of Exeter Department of Computer Science Exeter, United Kingdom · Jingdong Logistics Beijing, China · University of Warwick Department of Computer Science Coventry, United Kingdom
TaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Driving
Mar 6, 2026Mingyu Fan, Yi Liu, Hao Zhou +3Trajectory Reconstruction Trajectory Prediction
College of Information and Intelligent Science, Donghua University, Shanghai 201620 China. · School of Computing and Information Technology, Great Bay Institute for Advanced Study/Great Bay University, Dongguan, China, and also with the Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. · Chongqing Chang’an Technology Co., Ltd, Chongqing 401120 China. +1

Oct 22, 2026J/K move · Enter open · S save

Track finding and particle identification performance with particle physics foundation models

Shuhang Li, Haiwang Yu, Gabor Galgoczi, Yeonju Go+8

Columbia University, New York, NY 10027, USA · Brookhaven National Laboratory, Upton, NY 11973, USA · State University of New York, Stony Brook, New York, NY 11794, USA+1

Trajectory Reconstruction High-Energy Physics Physical Foundation Models

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Modern high energy nuclear and particle physics experiments produce large amounts of sparse data that must be reduced significantly in size before performing physics analysis. Recently, there has been an effort to evaluate Foundation Models as a potential tool for performing data analysis. These models have been shown to learn features in high dimensional datasets based on the multi-modal data they are trained on. To show their effectiveness at performing complex pattern recognition tasks, such as reconstructing charged particles in a magnetic field, we evaluated the performance of a Foundation Model based reconstruction on several downstream tasks with a dataset composed of spacepoints in a Time Projection Chamber. We show that the performance of the Foundation Model based data analysis significantly improves upon that of traditional methods, and furthermore can be adapted to learn additional tasks that are not necessarily related to the initial training objective.