Journal
EUROPEAN PHYSICAL JOURNAL PLUS
Volume 135, Issue 6, Pages -Publisher
SPRINGER HEIDELBERG
DOI: 10.1140/epjp/s13360-020-00497-3
Keywords
-
Categories
Funding
- Swiss National Science Foundation (SNF) [200020-182037]
- Swiss National Science Foundation (SNF) [200020_182037] Funding Source: Swiss National Science Foundation (SNF)
Ask authors/readers for more resources
In high energy physics, graph-based implementations have the advantage of treating the input data sets in a similar way as they are collected by collider experiments. To expand on this concept, we propose a graph neural network enhanced by attention mechanisms called ABCNet. To exemplify the advantages and flexibility of treating collider data as a point cloud, two physically motivated problems are investigated: quark-gluon discrimination and pileup reduction. The former is an event-by-event classification, while the latter requires each reconstructed particle to receive a classification score. For both tasks, ABCNet shows an improved performance compared to other algorithms available.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available