# A hybrid deep learning approach to vertexing

## TL;DR

This paper introduces a novel GPU-friendly deep learning algorithm for vertexing in high-luminosity LHCb conditions, transforming sparse hit data into dense representations for efficient primary vertex detection.

## Contribution

The paper presents a new hybrid deep learning method that converts sparse 3D hit data into dense 1D inputs for CNNs, improving vertexing efficiency in high-luminosity conditions.

## Key findings

- Achieved over 90% efficiency in vertex detection.
- Maintained less than 0.2 false positives per event.
- Demonstrated potential for visualization and further 3D reconstruction.

## Abstract

In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per event, and will adopt a purely software trigger. This has fueled increased interest in alternative highly-parallel and GPU friendly algorithms for tracking and reconstruction. We will present a novel prototype algorithm for vertexing in the LHCb upgrade conditions. We use a custom kernel to transform the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply Deep Learning techniques to find PV locations. By training networks on our kernels using several Convolutional Neural Network layers, we have achieved better than 90% efficiency with no more than 0.2 False Positives (FPs) per event. Beyond its physics performance, this algorithm also provides a rich collection of possibilities for visualization and study of 1D convolutional networks. We will discuss the design, performance, and future potential areas of improvement and study, such as possible ways to recover the full 3D vertex information.