An updated hybrid deep learning algorithm for identifying and locating primary vertices

TL;DR

This paper introduces an improved hybrid deep learning algorithm for primary vertex detection in high-energy physics experiments, achieving higher efficiency and lower false positive rates using advanced data transformation and training techniques.

Contribution

The paper presents a novel hybrid deep learning approach combined with conventional methods for vertex finding, specifically optimized for the LHCb experiment's upcoming Run 3 conditions.

Findings

Achieved over 94% efficiency in vertex detection

Reduced false positive rate significantly

Validated approach with full Run 3 MC data

Abstract

We present an improved hybrid algorithm for vertexing, that combines deep learning with conventional methods. Even though the algorithm is a generic approach to vertex finding, we focus here on it's application as an alternative Primary Vertex (PV) finding tool for the LHCb experiment. In the transition to Run 3 in 2021, LHCb will undergo a major luminosity upgrade, going from 1.1 to 5.6 expected visible PVs per event, and it will adopt a purely software trigger. 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 using proxy distributions to encode the truth in training data. Last year we reported that training networks on our kernels using several Convolutional Neural Network layers yielded better than 90 % efficiency with no more than 0.2 False Positives (FPs) per event. Modifying several elements of the algorithm, we now achieve better than 94 % efficiency with a significantly lower FP rate. Where our studies to date have been made using toy Monte Carlo (MC), we began to study KDEs produced from complete LHCb Run 3 MC data, including full tracking in the vertex locator rather than proto-tracking.