Abstract: The couplings of the electroweak effective theory contain information on the heavy-mass scales which are no-longer present in the low-energy Lagrangian. We build a general effective Lagrangian, implementing the electroweak chiral symmetry breaking SU(2)(L) circle times SU(2)(R) -> SU(2)(L+R), which couples the known particle fields to heavier states with bosonic quantum numbers J(P) = 0(+/-) and 1(+/-). We consider colour-singlet heavy fields that are in singlet or triplet representations of the electroweak group. Integrating out these heavy scales, we analyze the pattern of low-energy couplings among the light fields which are generated by the massive states. We adopt a generic non-linear realization of the electroweak symmetry breaking with a singlet Higgs, without making any assumption about its possible doublet structure. Special attention is given to the different possible descriptions of massive spin-1 fields and the differences arising from naive implementations of these formalisms, showing their full equivalence once a proper short-distance behaviour is required.

Abstract: The inclusive production of hadrons through electroweak currents can be rigorously analysed with short-distance theoretical tools. The associated observables are insensitive to the involved infrared behaviour of the strong interaction, allowing for very precise tests of Quantum Chromodynamics. The theoretical predictions for sigma(e(+)e(-) -> hadrons) and the hadronic decay widths of the tau lepton and the Z, W and Higgs bosons have reached an impressive accuracy of O(alpha(4)(s)). Precise experimental measurements of the Z and tau hadronic widths have made possible the accurate determination of the strong coupling at two very different energy scales, providing a highly significant experimental verification of asymptotic freedom. A detailed discussion of the theoretical description of these processes and their current phenomenological status is presented. The most precise determinations of alpha(s) from other sources are also briefly reviewed and compared with the fully-inclusive results.

Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.