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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
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Submission summary
| Authors (as registered SciPost users): | Barry M. Dillon · Eric Metodiev · Tilman Plehn · Jennifer Thompson |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/1902.09914v3 (pdf) |
| Date accepted: | July 25, 2019 |
| Date submitted: | July 24, 2019, 2 a.m. |
| Submitted by: | Tilman Plehn |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Theoretical, Computational |
Abstract
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)