2020/07/02 by Simon Akar, S. Akar, Akar, Simon +21 · 2 citations
Medicine · Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Instrumentation and Detectors (physics.ins-det) #Medical Imaging Techniques and Applications #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #hep-ex #physics.ins-det
paper · pdf · doi:10.48550/arxiv.2007.01023
4 pages, 3 figures
arxiv created 2020/07/02 · openalex publication_date 2020/07/02 · arxiv updated 2020/07/03 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
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.