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Workshop: NeurIPS 2023 Workshop: Machine Learning and the Physical Sciences

Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties

Lindsey Gray · Jennet Dickinson · Rachel Kovach-Fuentes · Morris Swartz · Giuseppe Di Guglielmo · Alice Bean · Douglas Berry · Manuel Blanco Valentin · Karri DiPetrillo · Farah Fahim · Jim Hirschauer · Shruti Kulkarni · Ron Lipton · Petar Maksimovic · Corrinne Mills · Mark Neubauer · Benjamin Parpillon · Gauri Pradhan · Chinar Syal · Nhan Tran · Jieun Yoo · Aaron Young


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 level-one 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.

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