Poster
in
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

[ Poster]

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 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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