Related papers: An updated hybrid deep learning algorithm for iden…
Deep learning is a rapidly-evolving technology with possibility to significantly improve physics reach of collider experiments. In this study we developed a novel algorithm of vertex finding for future lepton colliders such as the…
High-energy physics is facing increasingly computational challenges in real-time event reconstruction for the near-future high-luminosity era. Using the LHCb vertex detector as a use-case, we explore a new algorithm for particle track…
The next decade will see an order of magnitude increase in data collected by high-energy physics experiments, driven by the High-Luminosity LHC (HL-LHC). The reconstruction of charged particle trajectories (tracks) has always been a…
LHCb is one of the four main experiments of the Large Hadron Collider (LHC) project, which will start at CERN in 2008. The experiment is primarily dedicated to B-Physics and hence requires precise vertex reconstruction. The silicon vertex…
The data acquisition system of the upgraded LHCb experiment includes the fast reconstruction of all hits in the vertex locator (VELO) pixel detector at the beam-crossing rate of 40 MHz, implemented as on-the-fly clustering embedded in the…
The Large Hadron Collider beauty (LHCb) detector is designed to detect decays of b- and c- hadrons for the study of CP violation and rare decays. At the end of the LHC Run 2, many of the LHCb measurements remained statistically dominated.…
Reconstructing the trajectories of charged particles from the collection of hits they leave in the detectors of collider experiments like those at the Large Hadron Collider (LHC) is a challenging combinatorics problem and computationally…
This article describes a custom VHDL firmware implementation of a two-dimensional cluster-finder architecture for reconstructing hit positions in the new vertex pixel detector (VELO) that is part of the LHCb Upgrade. This firmware has been…
LHCb will commence data taking as the first dedicated heavy flavour experiment at a hadron collider in 2008. A very high hit precision from its vertex detector (VELO) is essential to meet the tight requirements of vertex reconstruction in…
Visual place recognition (VPR) is a fundamental task for many applications such as robot localization and augmented reality. Recently, the hierarchical VPR methods have received considerable attention due to the trade-off between accuracy…
In this paper, we make the very first attempt to investigate the integration of deep hash learning with vehicle re-identification. We propose a deep hash-based vehicle re-identification framework, dubbed DVHN, which substantially reduces…
We describe the Hybrid seeding, a standalone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by…
LHCb is the dedicated heavy flavour experiment at the Large Hadron Collider at CERN. The partially assembled silicon vertex locator (VELO) of the LHCb experiment has been tested in a beam test. The data from this beam test have been used to…
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in…
The LHCb (Large Hadron Collider beauty) experiment at CERN aims at achieving a significantly higher luminosity than originally planned by means of two major upgrades: the Upgrade I that took place during the Long Shutdown 2 (LS2) and the…
The Vertex Locator (VELO) is a silicon microstrip detector that surrounds the proton-proton interaction region in the LHCb experiment. The performance of the detector during the first years of its physics operation is reviewed. The system…
The LHCb detector at the LHC has shown a very successful initial operation and it is expected that the experiment will accumulate an integrated luminosity in proton-proton collisions of around 1 fb-1 in 2011. The data already collected are…
This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to…
Comprehending the environment and accurately detecting objects in 3D space are essential for advancing autonomous vehicle technologies. Integrating Camera and LIDAR data has emerged as an effective approach for achieving high accuracy in 3D…
The operating conditions defining the current data taking campaign at the Large Hadron Collider, known as Run 3, present unparalleled challenges for the real-time data acquisition workflow of the LHCb experiment at CERN. To address the…