期刊
JOURNAL OF HIGH ENERGY PHYSICS
卷 -, 期 12, 页码 -出版社
SPRINGER
DOI: 10.1007/JHEP12(2017)051
关键词
Jets
资金
- FAS Division of Science, Research Computing Group at Harvard University
- Office of Science of the U.S. Department of Energy (DOE) [DE-AC02-05CH11231, DE-SC0013607]
- DOE Office of Nuclear Physics [DE-SC0011090]
- DOE Office of High Energy Physics [DE-SC0012567]
- Microsoft Azure for Research award
- Harvard Data Science Initiative
Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.
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