Related papers: Machine Learning for Event Reconstruction in the C…
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range…
The High Luminosity Large Hadron Collider (HL-LHC) at CERN is expected to collide protons at a center-of-mass energy of 14 TeV and to reach the unprecedented peak instantaneous luminosity of $7.5 \times 10^{34} \text{cm}^{-2} \text{s}^{-1}$…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
Building particle tracks is the most computationally intense step of event reconstruction at the LHC. With the increased instantaneous luminosity and associated increase in pileup expected from the High-Luminosity LHC, the computational…
The CMS experiment at the LHC has started data taking in Run 3 at a $\mathrm{pp}$ collision energy of $13.6~\mathrm{TeV}$. In preparation for Run 3, detector systems, such as Pixel Tracker, HCAL and CSC, have been upgraded due to…
The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used…
The forthcoming Hyper-Kamiokande experiment requires substantially larger Monte Carlo datasets than previous experiments to satisfy stringent systematic-uncertainty requirements. While traditional maximum-likelihood reconstruction provides…
Analysis of the CMS data and the simulation prediction based on these results indicate that the performance of the current scintillators in the CMS Hadron Endcap Calorimeter (HE) tiles will degrade dramatically in the High Luminosity LHC…
In this article, we review the application of modern machine-learning (ML) techniques to boost the search for processes involving the top quarks at the LHC. We revisit the formalism of Convolutional Neural Networks (CNNs), Graph Neural…
The High Luminosity Large Hadron Collider (HL-LHC) will have a peak luminosity of $5\times10^{34} $cm$^{-2} $\,s$^{-1}$, five times higher than the design luminosity of the LHC. The hadronic ATLAS Tile Calorimeter TileCal) is a sampling…
The ALICE (A Large Ion Collider Experiment) detector yields a huge sample of data from different sub-detectors. On-line data processing is applied to select and reduce the volume of the stored data. ALICE applies a multi-level hardware…
A wealth of physics results have already been obtained from the LHC, due to the excellent performance of the collider and its experiments. Even more results are expected to be achievable in the phase of the high-luminosity LHC (HL-LHC). It…
An efficient and precise reconstruction of charged-particle tracks is crucial for the overall performance of the CMS experiment. During Run 2 of LHC, significant upgrades were made to the track reconstruction algorithms in order to…
Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discusses the potential benefits of employing Graph Neural Network…
The CMS Level-1 calorimeter trigger is being upgraded in two stages to maintain performance as the LHC increases pile-up and instantaneous luminosity in its second run. In the first stage, improved algorithms including event-by-event…
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for…
In 2017, the LHC (Large Hadron Collider) at CERN has obtained an astonishing result providing proton-proton collisions with an integrated luminosity of 50.25 fb-1. The CMS (Compact Muon Solenoid), a general-purpose detector operating at the…
A real-time autoencoder-based anomaly detection system using semi-supervised machine learning has been developed for the online Data Quality Monitoring system of the electromagnetic calorimeter of the CMS detector at the CERN LHC. A novel…
The trigger systems of the LHC detectors play a crucial role in determining the physics capabilities of the experiments. A reduction of several orders of magnitude of the event rate is needed to reach values compatible with the detector…
With the LHC successfully collecting data at 7 TeV, plans are actively advancing for a series of upgrades leading eventually to about five times the LHC design-luminosity some 10-years from now in the High-Luminosity LHC (HL-LHC) project.…