Model-Independent Prediction of Initial Geometry Parameters in Heavy Ion Collision Using Machine Learning Models
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0289-3_98
Reference5 articles.
1. Lin, Z.-W., Ko, C.M., Li, B.-A., Zhang, B., Pal, S.: Multiphase transport model for relativistic heavy ion collisions. Phys. Rev. C 72, 064901 (2005)
2. Saha, A., Dan, D., Sanyal, S.: Machine-learning model-driven prediction of the initial geometry in heavy-ion collision experiments. Phys. Rev. C 106, 014901 (2022)
3. Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 160 (2021)
4. Shen, C., Qiu, Z., Song, H., Bernhard, J., Bass, S., Heinz, U.: The iEBE-VISHNU code package for relativistic heavy-ion collisions. Comput. Phys. Commun. 199, 61–85 (2016)
5. Song, H., Heinz, U.: Causal viscous hydrodynamics in 2 + 1 dimensions for relativistic heavy-ion collisions. Phys. Rev. C 77, 064901 (2008)
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