Description
Title: DEEP NEURAL NETWORKS AND TRACK FINDING
Abstract: Fast and antiquated techniques are required for reconstructing the tracks of charged particles in high-energy physics experiments. The majority of algorithms are sequential, and as the number of tracks increases, the CPU power requirement quickly rises. Due to their ability to model intricate non-linear data dependencies and finish all tracks concurrently, neural networks can accelerate the process. In this paper, we present a method for reconstructing straight tracks in a toy two-dimensional model using a deep neural network. This approach will be used to analyze the experimental data produced by the Muon experiment at CERN.
Keywords: deep neural networks, machine learning, tracking, HEP
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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