Connecting The Dots / Intelligent Trackers 2017

6-9 March 2017
LAL-Orsay
Europe/Zurich timezone

Young Scientist Forum : Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

9 Mar 2017, 14:15
15m
LAL-Orsay

LAL-Orsay

11 : Using tracks

Speaker

Zihao Jiang (Stanford University (US))

Description

A novel b-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS Experiment. This talk presents the expected performance of the RNN based b-tagging in simulated $t \bar t$ and high $p_T$ $Z’ \rightarrow b \bar b$ events. The RNN based b-tagging processes properties of tracks associated to jets which are represented in sequences. In contrast to traditional impact-parameter-based b-tagging algorithms which assume the tracks of jets are independent from each other, RNN based b-tagging can exploit the spatial and kinematic correlations of tracks which are initiated from the same b-hadrons. The neural network nature of the tagging algorithm also allows the flexibility of extending input features to include more track properties than can be effectively used in traditional algorithms.

Primary author

Zihao Jiang (Stanford University (US))