Kavli Affiliate: Salman Habib
| First 5 Authors: Tupendra Oli, Wilkie Olin-Ammentorp, Xingfu Wu, Justin H. Qian, Vinod K. Sangwan
| Summary:
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.
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