SciPost Submission Page
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
| Submission information |
| Preprint Link: |
https://arxiv.org/abs/1902.09914v3
(pdf)
|
| Date accepted: |
2019-07-25 |
| Date submitted: |
2019-07-24 02:00 |
| Submitted by: |
Plehn, Tilman |
| Submitted to: |
SciPost Physics |
| Ontological classification |
| Academic field: |
Physics |
| Specialties: |
- High-Energy Physics - Experiment
- High-Energy Physics - Theory
- High-Energy Physics - Phenomenology
|
| 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.
