Co-Design of 2D Heterojunctions for Data Filtering in Tracking Systems

TL;DR

This paper presents a co-designed system combining specialized hardware and machine learning algorithms to efficiently filter data in high-energy physics tracking systems, addressing the challenge of massive data volumes.

Contribution

It introduces a novel co-design approach integrating a customized SVM kernel with van der Waals heterojunction devices for real-time data filtering in particle detectors.

Findings

High-accuracy SVM classification for particle data

Efficient implementation using heterojunction devices

Potential for scalable data processing in HEP experiments

Abstract

As particle physics experiments evolve to achieve higher energies and resolutions, handling the massive data volumes produced by silicon pixel detectors, which are used for charged particle tracking, poses a significant challenge. To address the challenge of data transport from high resolution tracking systems, we investigate a support vector machine (SVM)-based data classification system designed to reject low-momentum particles in real-time. This SVM system achieves high accuracy through the use of a customized mixed kernel function, which is specifically adapted to the data recorded by a silicon tracker. Moreover, this custom kernel can be implemented using highly efficient, novel van der Waals heterojunction devices. This study demonstrates the co-design of circuits with applications that may be adapted to meet future device and processing needs in high-energy physics (HEP) collider experiments.