Related papers: Charged Particle Tracking with Machine Learning on…
Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques. Machine learning methods are ubiquitous and have proven to be very…
Starting in two years from now, particle physics will enter a new regime in terms of energies and luminosities, thanks to the Large Hadron Collider (LHC) at CERN. This report summarizes the status of the preparations, both for the machine…
Hardware-based track reconstruction in the CMS and ATLAS trigger systems for the High-Luminosity LHC upgrade will provide unique capabilities. An overview is presented of earlier track trigger systems at hadron colliders, in particular for…
Protons and heavy-ion beams at unprecedented energies are brought into collisions in the CERN Large Hadron Collider for high-energy experiments. The LHC multi-stage collimation system is designed to provide protection against regular and…
Triggering long-lived particles at the first stage of the trigger system is very crucial in LLP searches to ensure that we do not miss them at the very beginning. The future High Luminosity runs of the Large Hardron Collider will have…
To investigate the fundamental nature of matter and its interactions, particles are accelerated to very high energies and collided inside detectors, producing a multitude of other particles that are scattered in all directions. As charged…
The e+e- linear collider physics programme sets highly demanding requirements on the accurate determination of charged particle trajectories close to their production point. A new generation of Vertex Trackers, based on different…
Proton-nucleus (p+A) collisions have long been recognized as a crucial component of the physics programme with nuclear beams at high energies, in particular for their reference role to interpret and understand nucleus-nucleus data as well…
This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time. For autonomous vehicles,…
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review…
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple…
The electron-proton collider HERA is being upgraded to provide higher luminosity from the end of the year 2001. In order to enhance the selectivity on exclusive processes a Fast Track Trigger (FTT) with high momentum resolution is being…
In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the…
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds…
The physics programme for a coming electron linear collider is dominated by events with final states containing many jets. We develop in this paper the opinion that the best approach is to optimise the independent measurement of the tracks…
High-energy physics experiments require fast and efficient methods for reconstructing the tracks of charged particles. The commonly used algorithms are sequential, and the required CPU power increases rapidly with the number of tracks.…
Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not…
With the rapid development of in-depth learning, neural network and deep learning algorithms have been widely used in various fields, e.g., image, video and voice processing. However, the neural network model is getting larger and larger,…
Convolutional Neural Networks (CNNs) are fundamental to deep learning, driving applications across various domains. However, their growing complexity has significantly increased computational demands, necessitating efficient hardware…
The analog signals generated in the read-out electronics of radiation detectors are shaped prior to the digitization in order to improve the signal to noise ratio (SNR). The real amplitude of the analog signal is then obtained using digital…