A combination is presented of the inclusive deep inelastic cross sections measured by the H1 and ZEUS Collaborations in neutral and charged current unpolarised e ^± p scattering at HERA during the period 1994-2000. The data span six orders of magnitude in negative four-momentum-transfer squared, Q ², and in Bjorken x. The combination method used takes the correlations of systematic uncertainties into account, resulting in an improved accuracy. The combined data are the sole input in a NLO QCD analysis which determines a new set of parton distributions, HERAPDF1.0, with small experimental uncertainties. This set includes an estimate of the model and parametrisation uncertainties of the fit result.

A measurement of theJ/ψandψ(2S) production cross sections in pp collisions at $ \sqrt {s} = 7{\text{TeV}} $ with the CMS experiment at the LHC is presented. The data sample corresponds to an integrated luminosity of 37 pb⁻¹. Using a fit to the invariant mass and decay length distributions, production cross sections have been measured separately for prompt and non-prompt charmonium states, as a function of the meson transverse momentum in several rapidity ranges and integrated in the kinematical regions considered in this study. In addition, cross sections restricted to the acceptance of the CMS detector are given, which are not affected by the polarization of the charmonium states. The ratio of the differential production cross sections of the two states, where systematic uncertainties largely cancel, is also determined. The branching fraction of the inclusive B →ψ(2S)Xdecay is extracted from the ratio of the non-prompt cross sections to be: $ \mathcal{B}\left( {{\text{B}} \to \psi \left( {\text{2S}} \right)X} \right) = \left( {{3}.0{8}\pm 0.{12}\left( {{\text{stat}}. + {\text{syst}}.} \right)\pm 0.{13}\left( {{\text{theor}}.} \right)\pm 0.{42}\left( {{\mathcal{B}_{\text{PDG}}}} \right)} \right) \times {1}{0^{{ - {3}}}}. $

A study of dijet production in proton–proton collisions was performed at $\sqrt{s}=7\mbox{~TeV}$ for jets with p_T>35 GeV and |y|<4.7 using data collected with the CMS detector at the LHC in 2010. Events with at least one pair of jets are denoted as “inclusive”. Events with exactly one pair of jets are called “exclusive”. The ratio of the cross section of all pairwise combinations of jets to the exclusive dijet cross section as a function of the rapidity difference between jets |Δy| is measured for the first time up to |Δy|=9.2. The ratio of the cross section for the pair consisting of the most forward and the most backward jet from the inclusive sample to the exclusive dijet cross section is also presented. The predictions of the Monte Carlo event generators pythia6 and pythia8 agree with the measurements. In both ratios the herwig++ generator exhibits a more pronounced rise versus |Δy| than observed in the data. The BFKL-motivated generators cascade and hej+ariadne predict for these ratios a significantly stronger rise than observed.

Bose-Einstein correlations between identical particles are measured in samples of proton-proton collisions at 0.9 and 7 TeV centre-of-mass energies, recorded by the CMS experiment at the LHC. The signal is observed in the form of an enhancement of number of pairs of same-sign charged particles with small relative momentum. The dependence of this enhancement on kinematic and topological features of the event is studied. Anticorrelations between same-sign charged particles are observed in the region of relative momenta higher than those in the signal region.

The safety and reliability of railway transport requires new solutions for monitoring and quick identification of faults in the railway infrastructure. Electric heating devices (EORs) are the crucial element of turnouts. EORs ensure heating during low temperature periods when ice or snow can lock the turnout device. Thermal imaging is a response to the need for an EOR inspection tool. After processing, a thermogram is a great support for the manual inspection of an EOR, or the thermogram can be the input for a machine learning algorithm. In this article, the authors review the literature in terms of thermographic analysis and its applications for detecting railroad damage, analysing images through machine learning, and improving railway traffic safety. The EOR device, its components, and technical parameters are discussed, as well as inspection and maintenance requirements. On this base, the authors present the concept of using thermographic imaging to detect EOR failures and malfunctions using a practical example, as well as the concept of using machine learning mechanisms to automatically analyse thermograms. The authors show that the proposed method of analysis can be an effective tool for examining EOR status and that it can be included in the official EOR inspection calendar.