Data processing method of silicon semiconductor vertex chamber based on impact point coordinate

Donghao Hong; Jingtong Zhen; Yuxuan Zhou; Zifeng Qiu

doi:10.54254/2755-2721/2024.20704

Article Information

Received: 23 August 2024

Published: 8 February 2025

DOI: https://doi.org/10.54254/2755-2721/2024.20704

Article Type: Research Article

Cite this article

Hong,D.;Zhen,J.;Zhou,Y.;Qiu,Z. (2025). Data processing method of silicon semiconductor vertex chamber based on impact point coordinate. Applied and Computational Engineering,132,172-182.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: ACE Vol.132

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

Copyright & License

[1]Bertacchi, V., Bilka, T., Braun, N., Casarosa, G., Corona, L., Cunliffe, S., Dattola, F., De Marino, G., De Nuccio, M., De Pietro, G., Dong, T.V., Dujany, G., Ecker, P., Eliachevitch, M., Fillinger, T., Frost, O., Frühwirth, R., Gebauer, U., Glazov, S., Gosling, N., Guo, A., Hauth, T., Heck, M., Kaleta, M., Kandra, J., Kleinwort, C., Kuhr, T., Kurz, S., Kvasnicka, P., Lettenbichler, J., Lueck, T., Martini, A., Metzner, F., Neverov, D., Niebuhr, C., Paoloni, E., Patra, S., Piilonen, L., Praz, C., Prim, M.T., Pulvermacher, C., Racs, S., Rad, N., Rados, P., Ritter, M., Rizzo, G., Rostomyan, A., Scavino, B., Schlüter, T., Schwenker, B., Spataro, S., Spruck, B., Svidras, H., Tenchini, F., Uematsu, Y., Webb, J., Wessel, C., Zani, L., 2020. Track finding at Belle II. ScienceDirect, Computer Physics Communications, pp.2.

[2]Billoir, P., Qian, S., 1990. Simultaneous PA recognition and C fitting filtering. Nuclear Instruments and Methods in Physics Research A, 294, pp.219-228. North-Holland. Received 6 March 1990.

[3]Steven Weinberg; Particle physics, from Rutherford to the LHC. Physics Today 1 August 2011; 64 (8): 29–32. https://doi.org/10.1063/PT.3.1216

[4]Liu, M. Y., Li, W. D., Huang, X. T., Zhang, Y., Lin, T., & Yuan, Y. (2024). Simulation and reconstruction of particle trajectories in the CEPC drift chamber. Nuclear Science and Techniques, 35(8), 128.

[5]CMS collaboration. (2014). Description and performance of track and primary-vertex reconstruction with the CMS tracker. Journal of Instrumentation, 9(10), P10009.

[6]Li, J., Qu, Z., & Xu, W. (2024). New vertex reconstruction algorithm for measuring gamma rays with the AMS tracker. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1058, 168835.

[7]Yin, L., Wang, Y., Chen, H., & Deng, W. (2022). An improved density peak clustering algorithm for multi-density data. Sensors, 22(22), 8814.

[8]Qasim, S. R., Kieseler, J., Iiyama, Y., & Pierini, M. (2019). Learning representations of irregular particle-detector geometry with distance-weighted graph networks. The European Physical Journal C, 79(7), 1-11.

[9]Kumar, K. M., & Reddy, A. R. M. (2016). A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recognition, 58, 39-48.

[10]Ye, Y., Zhu, J., & Wang, G. (2004). Geometric studies on variable radius spiral cone‐beam scanning. Medical Physics, 31(6), 1473-1480.

[11]Ribeiro, M.I., 2004. Gaussian probability density functions: Properties and error characterization. Institute for Systems and Robotics, Lisboa, Portugal.

[12]DeZoort, G., Thais, S., Duarte, J., Razavimaleki, V., Atkinson, M., Ojalvo, I., ... & Elmer, P. (2021). Charged particle tracking via edge-classifying interaction networks. Computing and Software for Big Science, 5, 1-13.

[13]Mondal, S., & Mastrolorenzo, L. (2024). Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC. arXiv preprint arXiv:2404.01071.

[14]Nawi, N. M., Atomi, W. H., & Rehman, M. Z. (2013). The effect of data pre-processing on optimized training of artificial neural networks. Procedia Technology, 11, 32-39.