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• W2896564495 endingPage "042023" @default.
• W2896564495 startingPage "042023" @default.
• W2896564495 abstract "Charged particle reconstruction in dense environments, such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms, such as the combinatorial Kalman Filter, have been used with great success in HEP experiments for years. However, these state-of-the-art techniques are inherently sequential and scale quadratically or worse with increased detector occupancy. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as FPGAs or GPUs. In this paper we present the evolution and performance of our recurrent (LSTM) and convolutional neural networks moving from basic 2D models to more complex models and the challenges of scaling up to realistic dimensionality/sparsity." @default.
• W2896564495 created "2018-10-26" @default.
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• W2896564495 date "2018-09-01" @default.
• W2896564495 modified "2023-10-17" @default.
• W2896564495 title "The HEP.TrkX Project: Deep Learning for Particle Tracking" @default.
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• W2896564495 doi "https://doi.org/10.1088/1742-6596/1085/4/042023" @default.
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