Towards Fast Displaced Vertex Finding
This work addresses the challenge of enabling trigger-level reconstruction of displaced vertices for searches of metastable particles in high-energy collisions, which is incremental as it is a first step using neural networks in an idealized setup.
The paper tackled the problem of computationally intensive displaced vertex reconstruction in particle physics by approximating the primary vertex location using a 4-layer dense neural network, achieving a precision of O(1 mm) RMS in low track multiplicity and O(20 mm) RMS in high track multiplicity environments.
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.