Related papers: A C++ program for estimating detector sensitivitie…

We describe the CASTOR detector designed to probe the very forward, baryon-rich rapidity region in nucleus-nucleus collisions at the LHC. We present a phenomenological model describing the formation of a QGP fireball in a high baryochemical…

The Compact Linear Collider, CLIC, is a multi-TeV electron-positron collider proposed for construction at CERN. A detector model, CLICdet, that is suited for the experimental conditions at CLIC and is based on realistic performance, has…

The DEPFET Collaboration pursues the development of a high resolution pixel vertex detector for future colliders (like ILC), based on the integration of amplifying transistors into a fully depleted bulk. In August 2008, six DEPFET…

Charge Coupled Devices (CCDs) have been successfully used in several high energy physics experiments over the past two decades. Their high spatial resolution and thin sensitive layers make them an excellent tool for studying short-lived…

I describe an new event generator, pandora, which uses the C++ class structure to allow a modular treatment of beams and particle production and decay. (Talk presented at the 1999 International Workshop on Linear Colliders, Sitges,…

The physics motivation for a very forward detector for the ALICE heavy ion experiment at the CERN LHC is discussed. A phenomenological model describing the formation and decay of a Centauro fireball in nucleus-nucleus collisions is…

Many models beyond the Standard Model predict light and feebly interacting particles that are often long-lived. These long-lived particles (LLPs) in many cases can be produced from meson decays. In this work, we propose a simple and quick…

Geometric predicates are at the core of many algorithms, such as the construction of Delaunay triangulations, mesh processing and spatial relation tests. These algorithms have applications in scientific computing, geographic information…

Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors…

The growing complexity of particle detectors makes their construction and quality control a new challenge. We present studies that explore the use of deep learning-based computer vision techniques to perform quality checks of detector…

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton…

We developed a portable code for dissipative particle dynamics (DPD) simulations. This Fortran program named CAMUS has a couple of notable features. One is the omission of constructing the so-called neighboring particles list, providing a…

The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…

REal-time Detection and STORage of Multy-chain events Builder C++ code has been designed to operate together with the new DGFRS detection system in FLNR, JINR. It allows to provide event by event storage and real-time search for ER-alpha…

Disaggregated memory is promising for improving memory utilization in computer clusters in which memory demands significantly vary across computer nodes under utilization. It allows applications with high memory demands to use memory in…

We present a next generation of multi-particle Monte Carlo (MC) Event generators for LHC and ILC for the MSSM, namely the three program packages Madgraph/MadEvent, WHiZard/O'Mega and Sherpa/Amegic++. The interesting but difficult…

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…

In this contribution, we evaluate the sensitivity for particles with charges much smaller than the electron charge with a dedicated scintillator-based detector in the far forward region at the CERN LHC, FORMOSA. This contribution will…

Machine learning (ML) tools such as encoder-decoder deep convolutional neural networks (CNN) are able to extract relationships between inputs and outputs of large complex systems directly from raw data. For time-varying systems the…

Position sensitive detectors based on gaseous scintillation proportional counters with Anger-type readout are being used in several research areas such as neutron detection, search for dark matter and neutrinoless double beta decay. Design…