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La Palma 2012

Lectures (23 h)

1.High Energy Physics and the Very Early Universe: Inflation and Quantum Cosmology (3h)

2. From Inflation to Nucleosynthesis (3 h)

  • K. Olive (univ. of Minnesota)
  • QCD implications on the Early Universe Baryon and Lepton number violation. CP Violation. Baryogenesis and Leptogenesis. Quark-hadron transition. Neutrino Cosmology. Primordial Nucleosynthesis

3. The Physics of Primary CMB Anisotropies. (3 h)

  •  A. Lasenby (Univ. of Cambridge)
  • Primordial inhomogeneities. Physics of Recombination. Seeds of structure and anisotropies,Gravito-acoustic oscillations. Angular power spectra.

4. Observations of the CMB spectrum and anisotropies: constraints on the Cosmological model. (3 h)

  • Paolo de Bernardis (Univ. of Roma)
  • CMB Experiments. Anisotropy measurements. Cosmological constraints. Foregrounds: synchrotron, free-free, thermal dust emission

5. Primordial Gravitational Waves and the Polarization of the CMB (2.5h)

  • J. A. Rubiño (Instituto de Astrofisica de Canarias)
  • Anisotropy: E and B modes. Primordial gravitational waves. Polarization experiments. Observations. Power spectra. Polarization of foregrounds.

6. The CMB interaction with high energy particles: hot plasmas in clusters of galaxies and in the intergalactic medium (3h)

  • S. Colafrancesco, (INAF, Roma)
  • CMB interactions with electrons and cosmic-ray particles.The thermal and Kinetic Sunyaev-Zeldovich effect in clusters of galaxies. SZ Experiments and observations. Fermi Bubles and the Haze. Diffuse gamma ray background.

7. CMB, Dark matter and Dark energy (2h)

  • J. García Bellido  (Univ. Autónoma de Madrid)
  • Dark matter: particles and experiments overview. Dark energy theories. Role of Dark matter and Dark energy on Structure Formation. What can we learn from CMB and Large Scale Structure

8. CMB data analysis techniques. (3h)

  • L.Verde (Instituto Ciencias del Cosmos, Barcelona)
  • From time ordered data to maps. Statistical tools. Bayesian Analysis. Component separation. Monte Carlo Markov Chains.