Development of a Vertex Finding Algorithm using Recurrent Neural Network

by   Kiichi Goto, et al.

Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the International Linear Collider. We deploy two networks; one is simple fully-connected layers to look for vertex seeds from track pairs, and the other is a customized Recurrent Neural Network with an attention mechanism and an encoder-decoder structure to associate tracks to the vertex seeds. The performance of the vertex finder is compared with the standard ILC reconstruction algorithm.


Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement

Speech enhancement has benefited from the success of deep learning in te...

Generating News Headlines with Recurrent Neural Networks

We describe an application of an encoder-decoder recurrent neural networ...

Dynamic Cell Structure via Recursive-Recurrent Neural Networks

In a recurrent setting, conventional approaches to neural architecture s...

Attention Based Neural Networks for Wireless Channel Estimation

In this paper, we deploy the self-attention mechanism to achieve improve...

An interpretable LSTM neural network for autoregressive exogenous model

In this paper, we propose an interpretable LSTM recurrent neural network...

A 0.16pJ/bit Recurrent Neural Network Based PUF for Enhanced Machine Learning Atack Resistance

Physically Unclonable Function (PUF) circuits are finding widespread use...

Catch and Prolong: recurrent neural network for seeking track-candidates

One of the most important problems of data processing in high energy and...