SavedAdd your journal

Journals / International Journal of Innovative Engineering Applications / 2021 / Cilt: 5 - Sayı: 2

Artificial Intelligence Based Machine Learning Approach in High Energy Physics

Serpil YALÇIN KUZU
Pages
176–180
Publication Date
2022-10-19
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this contribution, application of machine learning approaches in dielectron analysis is discussed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In high energy physics experiments data quality plays a significant role for particle identification.
📊
Key Findings & Data:Methods used in particle analysis are mainly based on high level knowledge and complex computation skills of human experts and require long time for data quality assurance.
💡
Conclusion & Impact:In this contribution, application of machine learning approaches in dielectron analysis is discussed.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In high energy physics experiments data quality plays a significant role for particle identification. Methods used in particle analysis are mainly based on high level knowledge and complex computation skills of human experts and require long time for data quality assurance. Artificial intelligence (AI) applications in various fields are getting important to improve the speed, accuracy and efficiency of human efforts. For this purpose, artificial intelligence-based machine learning approach can be used in particle physics analysis. Dielectrons (e-e+) are electromagnetic probes that provide information about evolution of the medium formed in high energy collisions due to lack of final state interactions. A high purity sample of e-e+ pairs can be obtained by traditional cut-based methods resulting in low efficiency. In this contribution, application of machine learning approaches in dielectron analysis is discussed.

🔖

Cite This Article

KUZU, S. Y. (2022). Artificial Intelligence Based Machine Learning Approach in High Energy Physics. International Journal of Innovative Engineering Applications, Cilt: 5 - Sayı: 2, 176–180. https://ajindex.com/articles/541232

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Markert, C. (2005). What do we learn from resonance production in heavy ion collisions?. Journal of Physics G: Nuclear and Particle Physics, 31(4), S169
  2. Torrieri, G., & Rafelski, J. (2001). Strange hadron resonances as a signature of freeze-out dynamics. Physics Letters B, 509(3-4), 239-245
  3. Bleicher, M., & Aichelin, J. (2002). Strange resonance production: Probing chemical and thermal freeze-out in relativistic heavy ion collisions. Physics Letters B, 530(1-4), 81-87
  4. Tawfik, A., & Shalaby, A. G. (2015). Balance function in high-energy collisions. Advances in High Energy Physics, 2015
  5. Rapp, R., & Wambach, J. (2002). Chiral symmetry restoration and dileptons in relativistic heavy-ion collisions. Advances in Nuclear Physics, 1-205
  6. Drell, S. D., & Yan, T. M. (1970). Massive lepton-pair production in hadron-hadron collisions at high energies. Physical Review Letters, 25(5), 316
  7. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 20(8), 832-844
  8. Trzcinski, T., Graczykowski, L. K. & Glinka, M. (2019). Using Random Forest Classifier for particle identification in the ALICE Experiment. Proceedings of Information Technology, Systems Research and Computational Physics, pp. 3–17
  9. Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R news, 2(3), 18-22
  10. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32
  11. Azhari, M., Alaoui, A., Achraoui, Z., Ettaki, B. & Zerouaoui, J. (2019). Adaptation of the Random Forest Method, Proceedings of the 4th International Conference on Smart City Applications, 1141–1146
  12. Azhari, M., Alaoui, A., Abarda, A., Ettaki, B., & Zerouaoui, J. (2019). Using ensemble methods to solve the problem of pulsar search. In International Conference on Big Data and Networks Technologies (pp. 183-189). Springer, Cham
  13. Azhari, M., Alaoui, A., Abarda, A., Ettaki, B., & Zerouaoui, J. (2019). A comparison of random forest methods for solving the problem of pulsar search. In The Proceedings of the Third International Conference on Smart City Applications (pp. 796-807). Springer, Cham
  14. Mbaabu, O. (2020). Introduction to Random Forest in Machine Learning, Retrieved February 19, 2021, from
  15. Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a g