Related papers: Charged Particle Tracking with Machine Learning on…
Low-energy strong interactions are a major source of background at hadron colliders, and methods of subtracting the associated energy flow are well established in the field. Traditional approaches treat the contamination as diffuse, and…
Diffractive phenomena constitute a large fraction of interactions occurring in pp collisions at LHC. Because of the non-perturbative nature, their present understanding is still relatively poor and uncertain. One of the methods to study…
Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised…
The discovery potential of both singlet and doublet vector-like leptons (VLLs) at the Large Hadron Collider (LHC) as well as at the not-so-far future muon and electron machines is explored. The focus is on a single production channel for…
We discuss the physics of large impact parameter interactions at the LHC: ultraperipheral collisions (UPCs). The dominant processes in UPCs are photon-nucleon (nucleus) interactions. The current LHC detector configurations can explore small…
After a highly successful first data taking period at the LHC, the LHCb experiment developed a new trigger strategy with a real-time reconstruction, alignment and calibration for Run II. This strategy relies on offline-like track…
The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The…
Since 2022, the LHCb detector has been taking both proton-proton and lead-ion data at the LHC collision rate using a fully software-based trigger. This has been implemented on GPUs at its first stage and CPUs at its second. The setup allows…
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…
We present track reconstruction algorithms based on deep learning, tailored to overcome specific central challenges in the field of hadron physics. Two approaches are used: (i) deep learning (DL) model known as fully-connected neural…
The forward proton detectors, already existing at the LHC, are considered in the context of heavy ion collisions. It is shown that such detectors have the potential to measure nuclear debris originating from spectator nucleons. The…
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical…
Collimation systems in particle accelerators are designed to dispose of unavoidable losses safely and efficiently during beam operation. Different roles are required for different types of accelerator. The present state of the art in beam…
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…
In hadron collider experiments, triggering the detector to store interesting events for offline analysis is a challenge due to the high rates and multiplicities of particles produced. Maintaining high trigger efficiency for the physics we…
We present a new publicly available dataset that contains simulated data of a novel calorimeter to be installed at the CERN Large Hadron Collider. This detector will have more than six-million channels with each channel capable of position,…
The LHCb experiment at the Large Hadron Collider (LHC) is performing high precision measurements in the flavour sector. An excellent performance of the particle identification (PID) detectors as well as the development of new data taking…
A precise measurement of the top-Higgs coupling is essential in particle physics, as it offers a powerful probe of potential new physics beyond the Standard Model (BSM), particularly scenarios involving ${\cal CP}$ violation, which is a key…
Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN…
Particle physicists at the Large Hadron Collider investigate the properties of matter at subatomic length scales by colliding together bunches of high-energy protons and observing the decay products of the collisions. ATLAS is one of two…