Abstract
Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks. Our results indicate that the CNN tagger is robust with respect to possible mis-modeling of soft and collinear final-state radiation by Monte Carlo generators.
| Original language | English |
|---|---|
| Article number | 132 |
| Journal | Journal of High Energy Physics |
| Volume | 2019 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
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Keywords
- Jets
- QCD Phenomenology
ASJC Scopus subject areas
- Nuclear and High Energy Physics
Cite this
Infrared safety of a neural-net top tagging algorithm. / Choi, Suyong; Lee, Seung Joon; Perelstein, Maxim.
In: Journal of High Energy Physics, Vol. 2019, No. 2, 132, 01.02.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Infrared safety of a neural-net top tagging algorithm
AU - Choi, Suyong
AU - Lee, Seung Joon
AU - Perelstein, Maxim
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks. Our results indicate that the CNN tagger is robust with respect to possible mis-modeling of soft and collinear final-state radiation by Monte Carlo generators.
AB - Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we construct a top-jet tagger based on a Convolutional Neural Network (CNN), and apply it to parton-level boosted top samples, with and without an additional gluon in the final state. We show that the jet observable defined by the CNN obeys the canonical definition of infrared safety: it is unaffected by the presence of the extra gluon, as long as it is soft or collinear with one of the quarks. Our results indicate that the CNN tagger is robust with respect to possible mis-modeling of soft and collinear final-state radiation by Monte Carlo generators.
KW - Jets
KW - QCD Phenomenology
UR - http://www.scopus.com/inward/record.url?scp=85062030659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062030659&partnerID=8YFLogxK
U2 - 10.1007/JHEP02(2019)132
DO - 10.1007/JHEP02(2019)132
M3 - Article
AN - SCOPUS:85062030659
VL - 2019
JO - Journal of High Energy Physics
JF - Journal of High Energy Physics
SN - 1126-6708
IS - 2
M1 - 132
ER -