Centro de Excelencia Severo Ochoa
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Eventos Recientes
Septiembre 12, 2024
De Septiembre 9, 2024 hasta Septiembre 13, 2024
| Speaker | Tipo de Evento | ||||
|---|---|---|---|---|---|
 | Dic 1 2022 - 15:00 | Ending inflation with a bang: Higgs vacuum decay in R + R^2 gravity | Andreas Mantziris | According to the current experimental data, the Higgs vacuum appears to be metastable due to the development of a second lower ground state in its potential. | Seminar |
 | Ene 12 2023 - 15:00 | LCDM Tensions, Gravitational Waves and Machine Learning | George Alestas | Although ΛCDM is the widely accepted standard model of Cosmology, there is an increasing number of tensions or discrepancies identified within it. | Seminar |
| Ene 19 2023 - 15:00 | Direct detection experiments as a laboratory for solar neutrino physics | Patrick Foldenauer | The next generation of dark matter (DM) direct detection (DD) experiments are becoming sensitive to the scattering of solar neutrinos. | Seminar |
| Ene 26 2023 - 15:00 | SUSY and Higgs Searches at the LHC and Future Collider | Cem Un | Even though the discovery of the Higgs boson is breakthrough for the modelsbeyond the Standard Model, it has also led to a strong impact especially in | Seminar |
| Ene 30 2023 - 15:00 | The Neutrino Magnetic Moment Portal: Phenomenology and UV Completions | Vedran Brdar | Sterile neutrinos are well-motivated extension of the Standard Model. | Seminar |
| Feb 2 2023 - 15:00 | Fractional quantum gravity | Gianluca Calcagni | We propose a class of UV-complete quantum field theories motivated by fractal geometry and based on the fractional d'Alembertian. | Seminar |
 | Feb 6 2023 - 15:00 | Precision Tests of the Standard Model and the Role of Lattice QCD | Hartmut Wittig | While the Standard Model of particle physics provides a quantitative description of the properties of the known constituents of visible matter, it fails to explain the existence of dark matter or t | Seminar |
| Feb 9 2023 - 15:00 | Discovery and exclusion limits without binning using machine-learned likelihoods | Andres D. Perez | Machine-Learned Likelihood is a method that, by combining the power of current machine-learning techniques with the likelihood-based inference tests used in traditional analyses, allows to estima | Seminar |
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