Machine learning for surface prediction in ACTS

08/06/2021

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We present an ongoing R D activity for machine-learning-assisted navigation through detectors to be used for track reconstruction. We investigate different approaches of training neural networks for surface prediction and compare their results. This work is carried out in the context of the ACTS tracking toolkit.

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Machine learning for surface prediction in ACTS

Sphere-Guided Training of Neural Implicit Surfaces

Using Artificial Intelligence for Particle Track Identification in CLAS12 Detector

Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

Iterative training of neural networks for intra prediction

Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC

Machine learning for surface prediction in ACTS

Related Research

Sphere-Guided Training of Neural Implicit Surfaces

Using Artificial Intelligence for Particle Track Identification in CLAS12 Detector

Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

Iterative training of neural networks for intra prediction

Training Physics-Informed Neural Networks via Multi-Task Optimization for Traffic Density Prediction

Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC