Multi-Scale Distributed Representation for Deep Learning and its Application to b-Jet Tagging
Year: 2018
Type: article
Abstract: Recently machine learning algorithms based on deep layered artificial neural networks (DNNs) have been applied to a wide variety of high energy physics problems such as jet tagging or event classification. We explore a simple but effective preprocessing step which transforms each realvalued observational quantity or input feature into a binary number with a fixed number of digits. Each binary digit represents the quantity or magnitude in different scales. We have shown that this approach improves the performance of DNNs significantly for some specific tasks without any further complication in feature engineering. We apply this multi-scale distributed binary representation to deep learning on b-jet tagging using daughter particles’ momenta and vertex information.
DOI: 10.3938/jkps.72.1292
Authors: Jason Sang Hun Lee, Inkyu Park, Sangnam Park
Institution: University of Seoul
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2806603846