Pileup Mitigation with Machine Learning (PUMML)
Name
13130_2017_Article_7248.pdf
Size
1.09 MB
Format
Adobe PDF
Checksum (MD5)
71e5d0b58d5c54b08558f9d2eb82c1fc
Author(s)
Nachman, Benjamin
Schwartz, Matthew D.
Komiske, Patrick T.
Metodiev, Eric Mario
Date Issued
December 2017
Journal
Journal of High Energy Physics
Publisher
Springer Berlin Heidelberg
Citation
Komiske, Patrick T., et al. “Pileup Mitigation with Machine Learning (PUMML).” Journal of High Energy Physics, vol. 2017, no. 12, Dec. 2017.
Version
Final published version
Abstract
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. Keywords: Jets.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/JHEP12(2017)051
