Jet cross sections have been measured for the first time in proton-proton collisions at a centre-of-mass energy of 7 TeV using the ATLAS detector. The measurement uses an integrated luminosity of 17 nb −1 recorded at the Large Hadron Collider. The anti-k t algorithm is used to identify jets, with two jet resolution parameters, R = 0.4 and 0.6. The dominant uncertainty comes from the jet energy scale, which is determined to within 7% for central jets above 60 GeV transverse momentum. Inclusive single-jet differential cross sections are presented as functions of jet transverse momentum and rapidity. Dijet cross sections are presented as functions of dijet mass and the angular variable χ. The results are compared to expectations based on next-toleading-order QCD, which agree with the data, providing a validation of the theory in a new kinematic regime.
We present GM2Calc, a public C++ program for the calculation of MSSM contributions to the anomalous magnetic moment of the muon, (g −2) μ . The code computes (g − 2) μ precisely, by taking into account the latest two-loop corrections and by performing the calculation in a physical on-shell renormalization scheme. In particular the program includes a tan β resummation so that it is valid for arbitrarily high values of tan β, as well as fermion/sfermion-loop corrections which lead to non-decoupling effects from heavy squarks. GM2Calc can be run with a standard SLHA input file, internally converting the input into on-shell parameters. Alternatively, input parameters may be specified directly in this on-shell scheme. In both cases the input file allows one to switch on/off individual contributions to study their relative impact. This paper also provides typical usage examples not only in conjunction with spectrum generators and plotting programs but also as C++ subroutines linked to other programs.
Fisheye cameras are commonly used in applications like autonomous driving and surveillance to provide a large field of view (> 180 • ). However, they come at the cost of strong non-linear distortion which require more complex algorithms. In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. Obtaining accurate and dense depth supervision is difficult in practice, but self-supervised learning approaches show promising results and could potentially overcome the problem. We present a novel self-supervised scale-aware framework for learning Euclidean distance and ego-motion from raw monocular fisheye videos without applying rectification. While it is possible to perform piece-wise linear approximation of fisheye projection surface and apply standard rectilinear models, it has its own set of issues like re-sampling distortion and discontinuities in transition regions. To encourage further research in this area, we will release this dataset as part of our WoodScape project [1]. We further evaluated the proposed algorithm on the KITTI dataset and obtained state-of-the-art results comparable to other self-supervised monocular methods. Qualitative results on an unseen fisheye video demonstrate impressive performance 1 .
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