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The rapid growth of data centers increasingly requires data center operators to "bring own generation" to complement the available utility power plants to supply all or part of data center load. This practice sharply increases the number of…
The Large Hadron Collider, LHC, though meant for discovery, will provide enough data from early phase to also perform various studies of Standard Model processes in as yet unexplored kinematic regions. Precision measurements of the…
In our former works we have made serious efforts to improve the performance of medical image analysis methods with using ensemble-based systems. In this paper, we present a novel hardware-based solution for the efficient adoption of our…
We introduce a framework that integrates both analytical and machine-learning approaches for calculating observables optimal for EFT and broader applications at the LHC. A new metric for evaluating the performance of these approaches has…
The FCC program at CERN provides an attractive all-in-one solution to address many of the key questions in particle physics. While we fully support the efforts towards this ambitious path, we believe that it is important to prepare a…
We propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems. Our scheme allows to efficiently employ compute accelerators such as GPUs and FPGAs for the training of…
CMS has developed a fast detector simulation package, which serves as a fast and reliable alternative to the detailed GEANT4-based (full) simulation, and enables efficient simulation of large numbers of standard model and new physics…
To extract physics results from the recorded data, the LHC experiments are using Grid computing infrastructure. The event data processing on the Grid requires scalable access to non-event data (detector conditions, calibrations, etc.)…
Today, deep learning is an essential technology for our life. To solve more complex problems with deep learning, both sizes of training datasets and neural networks are increasing. To train a model with large datasets and networks,…
While the particle physics community is eagerly waiting for a positive sign for the construction of the next energy frontier collider, developments continue to advance the detector capabilities. New methods and algorithms are being…
The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which…
The good performance of the LHC provided enough data at 7 TeV and 8 TeV to allow the experiments to perform very competitive measurements and to expand the knowledge about the fundamental interaction far beyond that from previous colliders.…
The computation of Lagrangian coherent structures (LCS) has become a standard tool for the analysis of advective transport in unsteady flow applications. LCS identification is primarily accomplished by evaluating measures based on the…
In this paper we propose a new approach for Big Data mining and analysis. This new approach works well on distributed datasets and deals with data clustering task of the analysis. The approach consists of two main phases, the first phase…
At the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), protons and heavy ions are accelerated to velocities close to the speed of light and collided in order to study particle interactions and give us…
The pace of improvement in the performance of conventional computer hardware has slowed significantly during the past decade, largely as a consequence of reaching the physical limits of manufacturing processes. To offset this slowdown, new…
Driven by the increasing volume of recorded data, the demand for simulation from experiments based at the Large Hadron Collider will rise sharply in the coming years. Addressing this demand solely with existing computationally intensive…
The Higgs self-coupling is crucial for understanding the structure of the scalar potential and the mechanism of electroweak symmetry breaking. In this work, utilizing deep neural network based on Particle Transformer that relies on…
The determination of charged particle trajectories (tracking) in collisions at the CERN Large Hadron Collider (LHC) is one of the most important aspects for event reconstruction at hadron colliders. This is especially true in the high…
HEP-Frame is a new C++ package designed to efficiently perform analyses of data sets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high performance servers and…