首页|New Artificial Intelligence Findings from Thomas Jefferson National Accelerator Facility Described (Charged Track Reconstruction with Artificial Intelligence fo r CLAS12)
New Artificial Intelligence Findings from Thomas Jefferson National Accelerator Facility Described (Charged Track Reconstruction with Artificial Intelligence fo r CLAS12)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting out of the Thomas Jeffer son National Accelerator Facility by NewsRx editors, research stated, “In this p aper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence.” Our news editors obtained a quote from the research from Thomas Jefferson Nation al Accelerator Facility: “In our approach, we use neural networks working togeth er to identify tracks based on the raw signals in the Drift Chambers. A Convolut ional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to i dentify tracks from combinations of clusters in the drift chambers. Our method i ncreases the tracking efficiency by 50% for multi-particle final s tates already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data qualit y.”
Thomas Jefferson National Accelerator Fa cilityArtificial IntelligenceEmerging TechnologiesMachine Learning
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