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Reports of My Demise Are Greatly Exaggerated: $N$-subjettiness Taggers Take On Jet Images
by Liam Moore, Karl Nordström, Sreedevi Varma, Malcolm Fairbairn
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Submission summary
| Authors (as registered SciPost users): | Sreedevi Varma |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/1807.04769v3 (pdf) |
| Date accepted: | 2019-09-04 |
| Date submitted: | 2019-08-08 02:00 |
| Submitted by: | Varma, Sreedevi |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Theoretical, Computational |
Abstract
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top quark decays. We find that they perform almost identically and are highly correlated once jet mass information is included, which suggests they are accessing the same underlying information which can be intuitively understood as being contained in 4-, 5-, 6-, and 8-body kinematic phase spaces depending on the sample. This suggests both of these methods are highly useful for heavy object tagging and provides a tentative answer to the question of what the image network is actually learning.
Published as SciPost Phys. 7, 036 (2019)
Submission & Refereeing History
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