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Pileup mitigation at the Large Hadron Collider with graph neural networks

Arjona Martínez, J. and Cerri, O. and Spiropulu, M. and Vlimant, J. R. and Pierini, M. (2019) Pileup mitigation at the Large Hadron Collider with graph neural networks. European Physical Journal Plus, 134 (7). Art. No. 333. ISSN 2190-5444. doi:10.1140/epjp/i2019-12710-3.

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At the Large Hadron Collider, the high-transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low-transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm (D. Bertolini et al., JHEP 10, 059 (2014)), employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.

Item Type:Article
Related URLs:
URLURL TypeDescription ReadCube access
Cerri, O.0000-0002-2191-0666
Spiropulu, M.0000-0001-8172-7081
Vlimant, J. R.0000-0002-9705-101X
Pierini, M.0000-0003-1939-4268
Additional Information:© 2019 Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature. First Online: 17 July 2019.
Issue or Number:7
Record Number:CaltechAUTHORS:20190715-101017091
Persistent URL:
Official Citation:Arjona Martínez, J., Cerri, O., Spiropulu, M. et al. Eur. Phys. J. Plus (2019) 134: 333.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97143
Deposited By: Tony Diaz
Deposited On:15 Jul 2019 17:17
Last Modified:16 Nov 2021 17:27

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