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The Machine Learning Landscape of Top Taggers

by G. Kasieczka, T. Plehn, A. Butter, K. Cranmer, D. Debnath, B. M. Dillon, M. Fairbairn, D. A. Faroughy, W. Fedorko, C. Gay, L. Gouskos, J. F. Kamenik, P. T. Komiske, S. Leiss, A. Lister, S. Macaluso, E. M. Metodiev, L. Moore, B. Nachman, K. Nordstrom, J. Pearkes, H. Qu, Y. Rath, M. Rieger, D. Shih, J. M. Thompson, S. Varma

Submission summary

As Contributors: Eric Metodiev · Tilman Plehn · Jennifer Thompson
Arxiv Link: (pdf)
Date accepted: 2019-07-25
Date submitted: 2019-07-24 02:00
Submitted by: Plehn, Tilman
Submitted to: SciPost Physics
Academic field: Physics
  • High-Energy Physics - Experiment
  • High-Energy Physics - Theory
  • High-Energy Physics - Phenomenology
Approaches: Experimental, Theoretical, Computational


Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

Published as SciPost Phys. 7, 014 (2019)

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