학술논문
Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties
Document Type
Working Paper
Author
Dickinson, Jennet; Kovach-Fuentes, Rachel; Gray, Lindsey; Swartz, Morris; Di Guglielmo, Giuseppe; Bean, Alice; Berry, Doug; Valentin, Manuel Blanco; DiPetrillo, Karri; Fahim, Farah; Hirschauer, James; Kulkarni, Shruti R.; Lipton, Ron; Maksimovic, Petar; Mills, Corrinne; Neubauer, Mark S.; Parpillon, Benjamin; Pradhan, Gauri; Syal, Chinar; Tran, Nhan; Wen, Dahai; Yoo, Jieun; Young, Aaron
Source
Subject
Language
Abstract
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-generation pixel sensors will be sufficiently fine-grained to determine angular information of the charged particle passing through from pixel-cluster properties. This detector technology immediately improves the situation for offline tracking, but any major improvements in physics reach are unrealized since they are dominated by lowest-level hardware trigger acceptance. We will demonstrate track angle and hit position prediction, including errors, using a mixture density network within a single layer of silicon as well as the progress towards and status of implementing the neural network in hardware on both FPGAs and ASICs.
Comment: 6 pages, 3 figures, submitted to Neural Information Processing Systems 2023 (NeurIPS)
Comment: 6 pages, 3 figures, submitted to Neural Information Processing Systems 2023 (NeurIPS)