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Speaker Tipo de Evento
Feb 1 2023 - 11:00 Cosmology/gravity journal club Join the IFT cosmology/gravity journal club at https://benty-fields.com to vote for papers and volunteer to present them. CosmoGrav Journal Club
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 2 2023 - 17:00 Particle Creation by Black Holes Eduardo Velasco Eduardo Velasco Aja will present the following paper: Particle Creation by Black Holes, by Stephen Hawking (Commun.Math.Phys. 43 (1975) 199-220).  Old Journal Club
Feb 3 2023 - 11:30 Pheno-Coffee An informal meeting where all the seniors and students are invited to discuss about phenomenology. Pheno-Coffee
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 6 2023 - 16:30 Microscopic origin of the entropy of black holes in general relativity Martín Sasieta It has been known for a long time that the local description of the interior of the black hole is in clear tension with its universal entropy, given by the Bekenstein-Hawking formula. HoloClub
Feb 8 2023 - 11:00 Cosmology/gravity journal club Join the IFT cosmology/gravity journal club at https://benty-fields.com to vote for papers and volunteer to present them. CosmoGrav Journal Club
Feb 9 2023 - 12:00 Reflections on Parity Breaking Ignacio Ruiz Ignacio will present the following paper (https://arxiv.org/abs/2212.00039): SPLE-Club
Feb 9 2023 - 14:00 Gravitational Anomalies Pablo Saura Bastida Pablo Saura Bastida (Universidad Politécnica de Cartagena & IFT) will present the paper "Gravitational Anomalies" by Luis Álvarez-Gaumé and Edward Witten (Nuclear Physics B234 (1983) 269- Old Journal Club
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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