# Development of a Vertex Finding Algorithm using Recurrent Neural Network

arXiv: https://arxiv.org/abs/2101.11906 · HTML: https://mlsanity.com/paper/2101.11906/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 28 Jan 2021.

## Builds on (most cited first, 3 of 3)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Machine Learning in High Energy Physics Community White Paper](https://mlsanity.com/paper/1807.02876/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml](https://mlsanity.com/paper/2207.00559/) (2022)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Differentiable Vertex Fitting for Jet Flavour Tagging](https://mlsanity.com/paper/2310.12804/) (2023)

## Abstract (arXiv)

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.