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

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.

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BibTeX

@article{dickinson2023smartpixels,
    author = "Dickinson, Jennet and Kovach-Fuentes, Rachel and Gray, Lindsey and Swartz, Morris and Di Guglielmo, Giuseppe and Bean, Alice and Berry, Doug and Valentin, Manuel Blanco and DiPetrillo, Karri and Fahim, Farah and others",
    title = "Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties",
    journal = "arXiv preprint arXiv:2312.11676",
    year = "2023"
}

Recommended citation: Jennet Dickinson, Rachel Kovach-Fuentes, Lindsey Gray, Morris Swartz, Giuseppe Di Guglielmo, Alice Bean, Doug Berry, Manuel Blanco Valentin, Karri DiPetrillo, Farah Fahim, others (2023). "Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties." arXiv preprint arXiv:2312.11676.
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