B Physics at the Z0 Resonance

B physics results from e+ e- annihilation at the Z0 resonance are reviewed. A vast program is summarised, including the study of B+, B0d, B0s and b baryon lifetimes, the time dependence of B0d and B0s oscillations, the width difference in the B0s system, and the measurements of the magnitudes of the CKM matrix elements Vcb and Vub.Comment: 17 pages, 8 figures, presented at the UK Phenomenology Workshop on Heavy Flavour and CP Violation, 17-22 September 200

Resolved Photon Processes

We review the present level of knowledge of the hadronic structure of the photon, as revealed in interactions involving quarks and gluons ``in" the photon. The concept of photon structure functions is introduced in the description of deep--inelastic $e \gamma$ scattering, and existing parametrizations of the parton densities in the photon are reviewed. We then turn to hard \gamp\ and \gaga\ collisions, where we treat the production of jets, heavy quarks, hard (direct) photons, \jpsi\ mesons, and lepton pairs. We also comment on issues that go beyond perturbation theory, including recent attempts at a comprehensive description of both hard and soft \gamp\ and \gaga\ interactions. We conclude with a list of open problems.Comment: LaTeX with equation.sty, 85 pages, 29 figures (not included). A complete PS file of the paper, including figures, can be obtained via anonymous ftp from ftp://phenom.physics.wisc.edu/pub/preprints/1995/madph-95-898.ps.

Machine learning at the energy and intensity frontiers of particle physics

International audienceOur knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics