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Flavor and Origin of Matter - Sabor y Origen de la Materia

SOM (We are) is one of the leading theoretical particle physics groups in Spain, and includes IFIC members from the University of Valencia and CSIC. Our group has a broad research program in flavor physics with active work in Lattice QCD, neutrino phenomenology and leptogenesis. Members of the group also carry out research in cosmology and fundamental physics. While we have a strong theoretical component to our research, some of us are involved in large collaborations in neutrino physics ( Euronu ) and cosmology (SDSSIII ).

SOM is the most active group at IFIC organizing about half of the IFIC collloquia and IFIC seminars (see La Trobada and Invisibles webinar).

 

Latest 10 Publications in INSPIRE

More publications:

Atmospheric neutrinos and new physics - Global fits of GUT-scale SUSY models with GAMBIT - The seesaw portal in testable models of neutrino masses - Can interacting dark energy solve the $H_0$ tension? - Cosmological searches for a non-cold dark matter component - Comment on "Strong Evidence for the Normal Neutrino Hierarchy" - Strong Bayesian Evidence for the Normal Neutrino Hierarchy - Constraints on warm dark matter from the ionization history of the Universe - The running of featureful primordial power spectra - Unveiling $\nu$ secrets with cosmological data: neutrino masses and mass hierarchy - Cold dark matter plus not-so-clumpy dark relics - This year's holiday present: Supersymmetry with Dark Matter is still natural - Hiding neutrino mass in modified gravity cosmologies - A new Generation of Standard Solar Models - A novel approach to quantifying the sensitivity of current and future cosmological datasets to the neutrino mass orderin... - Search for Magnetic Monopoles with the MoEDAL Forward Trapping Detector in 13 TeV Proton-Proton Collisions at the LHC - Testable Baryogenesis in Seesaw Models (NuFact 2016) - New Physics in Astrophysical Neutrino Flavor (NuFact 2016) - The seesaw path to leptonic CP violation - Accelerating the BSM interpretation of LHC data with machine learning

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