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On-chip probabilistic inference for charged-particle tracking at the sensor edge

Fuente: arXiv

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Auteurs principaux:	Das, Arghya Ranjan, Jiang, David, Kovach-Fuentes, Rachel, Kuang, Shiqi, Muñoz, Ana Sofía Calle, Shekar, Danush, Dickinson, Jennet, Di Guglielmo, Giuseppe, Gray, Lindsey, Liu, Mia, Mills, Corrinne, Neubauer, Mark S., Abadjiev, Daniel, Badea, Anthony, Berry, Doug, DiPetrillo, Karri, Fahim, Farah, Gandrakota, Abhijith, Gupta, Harshul, Hirschauer, James, Howard, Eliza, Lipton, Ron, Maksimovic, Petar, Manganelli, Nick, Parpillon, Benjamin, Pearkes, Jannicke, Silvestre, Ricardo, Swartz, Morris, Syal, Chinar, Tran, Nhan, Trivedi, Amit, Ulmer, Keith, Wadud, Mohammad Abrar, Weiss, Benjamin, You, Eric
Format:	Preprint
Publié:	2026
Sujets:	Instrumentation and Detectors High Energy Physics - Experiment
Accès en ligne:	Acceder al recurso
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author	Das, Arghya Ranjan Jiang, David Kovach-Fuentes, Rachel Kuang, Shiqi Muñoz, Ana Sofía Calle Shekar, Danush Dickinson, Jennet Di Guglielmo, Giuseppe Gray, Lindsey Liu, Mia Mills, Corrinne Neubauer, Mark S. Abadjiev, Daniel Badea, Anthony Berry, Doug DiPetrillo, Karri Fahim, Farah Gandrakota, Abhijith Gupta, Harshul Hirschauer, James Howard, Eliza Lipton, Ron Maksimovic, Petar Manganelli, Nick Parpillon, Benjamin Pearkes, Jannicke Silvestre, Ricardo Swartz, Morris Syal, Chinar Tran, Nhan Trivedi, Amit Ulmer, Keith Wadud, Mohammad Abrar Weiss, Benjamin You, Eric
author_facet	Das, Arghya Ranjan Jiang, David Kovach-Fuentes, Rachel Kuang, Shiqi Muñoz, Ana Sofía Calle Shekar, Danush Dickinson, Jennet Di Guglielmo, Giuseppe Gray, Lindsey Liu, Mia Mills, Corrinne Neubauer, Mark S. Abadjiev, Daniel Badea, Anthony Berry, Doug DiPetrillo, Karri Fahim, Farah Gandrakota, Abhijith Gupta, Harshul Hirschauer, James Howard, Eliza Lipton, Ron Maksimovic, Petar Manganelli, Nick Parpillon, Benjamin Pearkes, Jannicke Silvestre, Ricardo Swartz, Morris Syal, Chinar Tran, Nhan Trivedi, Amit Ulmer, Keith Wadud, Mohammad Abrar Weiss, Benjamin You, Eric
contents	Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_15946
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	On-chip probabilistic inference for charged-particle tracking at the sensor edge Das, Arghya Ranjan Jiang, David Kovach-Fuentes, Rachel Kuang, Shiqi Muñoz, Ana Sofía Calle Shekar, Danush Dickinson, Jennet Di Guglielmo, Giuseppe Gray, Lindsey Liu, Mia Mills, Corrinne Neubauer, Mark S. Abadjiev, Daniel Badea, Anthony Berry, Doug DiPetrillo, Karri Fahim, Farah Gandrakota, Abhijith Gupta, Harshul Hirschauer, James Howard, Eliza Lipton, Ron Maksimovic, Petar Manganelli, Nick Parpillon, Benjamin Pearkes, Jannicke Silvestre, Ricardo Swartz, Morris Syal, Chinar Tran, Nhan Trivedi, Amit Ulmer, Keith Wadud, Mohammad Abrar Weiss, Benjamin You, Eric Instrumentation and Detectors High Energy Physics - Experiment Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.
title	On-chip probabilistic inference for charged-particle tracking at the sensor edge
topic	Instrumentation and Detectors High Energy Physics - Experiment
url	https://arxiv.org/abs/2602.15946