most citedProgress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

physics.ins-det2024

Intelligent Pixel Detectors: Towards a Radiation Hard ASIC with On-Chip Machine Learning in 28 nm CMOS

Anthony Badea, Alice Bean, Doug Berry +16

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly g…

hep-ex2023

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

Jennet Dickinson, Rachel Kovach-Fuentes, Lindsey Gray +20

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-g…

physics.ins-det2023

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

Jieun Yoo, Jennet Dickinson, Morris Swartz +19

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced,…

physics.ins-det2023★ 4 cited

Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming

Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…

cs.NE2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

Shruti R. Kulkarni, Aaron Young, Prasanna Date +12

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experim…