Towards Fast Displaced Vertex Finding

TL;DR

This paper proposes a neural network-based method for rapid primary vertex localization in particle detectors, aiming to improve real-time detection of displaced vertices indicative of new physics phenomena.

Contribution

It introduces a dense neural network approach for fast primary vertex regression, enabling trigger-level displaced vertex reconstruction in idealized detector conditions.

Findings

Achieves approximately 1 mm RMS precision in low track multiplicity environments.

Attains about 20 mm RMS precision in high track multiplicity environments.

Demonstrates potential for real-time displaced vertex detection in high-energy physics experiments.

Abstract

Many Standard Model extensions predict metastable massive particles that can be detected by looking for displaced decay vertices in the inner detector volume. Current approaches to search for these events in high-energy particle collisions rely on the presence of additional energetic signatures to make an online selection during data-taking, as the reconstruction of displaced vertices is computationally intensive. Enabling trigger-level reconstruction of displaced vertices could significantly enhance the reach of such searches. This work is a first step approximating the location of the primary vertex in an idealised detector geometry using a 4-layer dense neural networks for regression of the vertex location yielding a precision of $O(1\ \mathrm{mm})$ [$O(20\ \mathrm{mm})$] RMS in a low [high] track multiplicity environment.