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"Large Hadron Collider"
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Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade
A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.
Journal Article
High-energy heavy ion collisions in the ALICE experiment at LHC-CERN: an overview
The ALICE experiment at CERN has been taking data with pp, pPb, XeXe, and PbPb collisions at various collision energies at the Large Hadron Collider. ALICE, equipped with detectors for hadrons, photons and (di)leptons, is a dedicated setup to study the formation and characterization of the de-confined state of quarks and gluons created in high-energy heavy ion collisions. During the last decade of data taking, ALICE has confirmed the creation of a partonic medium in heavy ion collisions through measurements of conventional and rare probes. In addition, in high-multiplicity pp and pPb collisions, ALICE also observed a few features that are similar to those found in PbPb collisions. We have discussed some of those results in collisions of both small and large systems that reveal the properties of the media created in pp, pPb, and PbPb collisions.
Journal Article
Detector simulation in LHC experiments and India
Experiments in the field of high energy physics make use of very complex detector systems. Extraction of meaningful results from these experiments needs a deep understanding of the detector performance. The most important tool in the process of understanding is the use of simulation of some known physics processes. Tools were being developed from late 1970s with more and more improved modeling of electromagnetic and strong interaction of particles with matter. These tools are also used to design a detector system of present day high energy physics experiment. Design and construction of detectors for the experiments at the Large Hadron Collider (
Lhc
) took nearly two decades. Detector simulation played an important role in these designs. One of the most important toolkits and its use in one of the experiments at the
Lhc
are described in this paper.
Journal Article
Indian contributions to LHC theory
Indian scientists began to work on the theoretical aspects of LHC physics from the early 1980s, at the same time when the rest of the world started taking interest in this then-futuristic topic. From this point grew a whole school of collider phenomenologists, who now form a significant fraction of the Indian high-energy physics community. This article briefly reviews the growth and contributions of the Indian school, on the way describing some of the physics ideas while placing the work in the international context, and proceeding thus, brings the story up to date in mid-2022.
Journal Article
