arXiv subjects

Tupendra Oli

Publications and source records attributed to Tupendra Oli.

3 recordsLinked to original sources

arXiv2024

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

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.

physics.ins-det↗

arXiv2024

An Autotuning-based Optimization Framework for Mixed-kernel SVM Classifications in Smart Pixel Datasets and Heterojunction Transistors

Support Vector Machine (SVM) is a state-of-the-art classification method widely used in science and engineering due to its high accuracy, its ability to deal with high dimensional data, and its flexibility in modeling diverse sources of data. In this paper, we propose an autotuning-based optimization framework to quantify the ranges of hyperparameters in SVMs to identify their optimal choices, and apply the framework to two SVMs with the mixed-kernel between Sigmoid and Gaussian kernels for smart pixel datasets in high energy physics (HEP) and mixed-kernel heterojunction transistors (MKH). Our experimental results show that the optimal selection of hyperparameters in the SVMs and the kernels greatly varies for different applications and datasets, and choosing their optimal choices is critical for a high classification accuracy of the mixed kernel SVMs. Uninformed choices of hyperparameters C and coef0 in the mixed-kernel SVMs result in severely low accuracy, and the proposed framework effectively quantifies the proper ranges for the hyperparameters in the SVMs to identify their optimal choices to achieve the highest accuracy 94.6\% for the HEP application and the highest average accuracy 97.2\% with far less tuning time for the MKH application.

cs.LG↗

arXiv2020

Prospect of undoped inorganic crystals at 77 Kelvin for low-mass dark matter search at Spallation Neutron Source

Investigated in this work were sensitivities of a prototype detector for the detection of low-mass dark matter particles produced at the Spallation Neutron Source at the Oak Ridge National Laboratory in two years of data taking. The presumed prototype consisted of 10 kg undoped CsI or NaI scintillation crystals directly coupled with SiPM arrays operated at 77 K. Compared to the COHERENT CsI(Na) detector, a much higher light yield was assumed for the prototype. An experiment with a cylindrical 1~kg undoped CsI crystal coupled directly to two photomultiplier tubes at about 77~K was conducted as the first step to verify the idea. A light yield of $26.0 \pm 0.4$ photoelectrons per keV electron-equivalent was achieved. This eliminated the concern of self light absorption in large crystals raised in some of the early studies.

physics.ins-det↗