Piecewise Linear Wilson lines

Wilson lines, being comparators that render non-local operator products gauge invariant, are extensively used in QCD calculations, especially in small-$x$ calculations, calculations concerning validation of factorisation schemes and in calculations for constructing or modelling parton density functions. We develop an algorithm to express piecewise path ordered exponentials as path ordered integrals over the separate segments, and apply it on linear segments, reducing the number of diagrams needed to be calculated. We show how different linear path topologies can be related using their colour structure. This framework allows one to easily switch results between different Wilson line structures, which is especially useful when testing different structures against each other, e.g. when checking universality properties of non-perturbative objects.Comment: Proceedings for Transversity 2014, 6 page

Evolution and dynamics of cusped light-like Wilson loops

We discuss the possible relation between the singular structure of TMDs on the light-cone and the geometrical behaviour of rectangular Wilson loops.Comment: 6 pages, proceedings for the 3rd Workshop on the QCD Structure of the Nucleon (QCD-N'12

Working With Wilson Lines

We present an algorithm to express Wilson lines that are defined on piecewise linear paths in function of their individual segments, reducing the number of diagrams needed to be calculated. The important step lies in the observation that different linear path topologies can be related to each other using their color structure. This framework allows one to easily switch results between different Wilson line topologies, which is helpful when testing different structures against each other.Comment: Proceedings for SPIN 2014, 6 page

Description of the luminosity evolution for the CERN LHC including dynamic aperture effects. Part I: the model

In recent years, modelling the evolution of beam losses in circular proton machines starting from the evolution of the dynamic aperture has been the focus of intense research. Results from single-particle, non-linear beam dynamics have been used to build simple models that proved to be in good agreement with beam measurements. These results have been generalised, thus opening the possibility to describe also the luminosity evolution in a circular hadron collider. In this paper, the focus is on the derivation of scaling laws for luminosity, which include both burn off and additional pseudo-diffusive effects. It is worthwhile stressing that time-dependence of some beam parameters can be taken into account in the proposed framework. The proposed models are applied to the analysis of a subset of the data collected during the CERN Large Hadron Collider (LHC) Run~1 in a companion paper (Part II)

Collimation performance of the 400MJ LHC beam at 6.8 TeV

During the third operational run of the Large Hadron Collider at CERN, starting in 2022, the beam energy was increased to 6.8 TeV and the bunch population is planned to be pushed to unprecedented levels. Already in the first year of operation, stored beam energies up to 400 MJ were achieved. An improvement in cleaning performance of the LHC collimation system is hence required. In this paper we review the collimation system performance during 2022, and compare it to previous years. Particular attention is given to the performance during beta*-levelling, which is part of the nominal cycle in Run 3. The performance of the automatic alignment tools is also discussed. Finally, we review the stability of the collimation system, which was monitored regularly during the run for all machine configurations to ensure the continued adequate functionality of the system

Application of machine learning techniques at the CERN Large Hadron Collider

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for years already. Accelerator physics, however, has not started exploiting machine learning until very recently. Several activities are flourishing in this domain, in view of providing new insights to beam dynamics in circular accelerators, in different laboratories worldwide. This is, for instance, the case for the CERN Large Hadron Collider, where since a few years exploratory studies are being carried out. A broad range of topics have been addressed, such as anomaly detection of beam position monitors, analysis of optimal correction tools for linear optics, optimisation of the collimation system, lifetime and performance optimisation, and detection of hidden correlations in the huge data set of beam dynamics observables collected during the LHC Run 2. Furthermore, very recently, machine learning techniques are being scrutinised for the advanced analysis of numerical simulations data, in view of improving our models of dynamic aperture evolution.peer-reviewe

Magnetization on rough ferromagnetic surfaces

Journal ArticleUsing Ising-model Monte Carlo simulations, we show a strong dependence of surface magnetization on surface roughness. On ferromagnetic surfaces with spin-exchange coupling larger than that of the bulk, the surface magnetic ordering temperature decreases toward the bulk Curie temperature with increasing roughness. For surfaces with spin-exchange coupling smaller than that of the bulk, a crossover behavior occurs: at low temperature, the surface magnetization decreases with increasing roughness; at high temperature, the reverse is true