This website provides up-to-date information on the seminars of the Hadronic, Nuclear and Atomic Physics group at the University of Barcelona. Seminars typically take place on Wednesdays at noon (12pm) at the Pere Pascual seminar room (V507) and are broadcast online. Please contact us (<sergig@icc.ub.edu>) if you need login details.
Semester 1 (2025/26 year)
May 13, Jack Jenkins (Universität Siegen) - Rare Semileptonic Decays from Kaons to B Mesons
May 18, Balma Duch (IFAE-UAB) - Padé Approximants for Noise Filtering for Experimental Data
May 28, Daniel Arturo López Aguilar (Cinvestav) - TBC
June 3, TBC
June 9, Paul-Gerhard Reinhard (U. Erlangen-Nuremberg) - TBA
June 17, Lars Zurek (CEA Saclay) - TBA
This website provides up-to-date information on the seminars of the Hadronic, Nuclear and Atomic Physics group at the University of Barcelona. Seminars typically take place on Wednesdays at noon (12pm) at the Pere Pascual seminar room (V507) and are broadcast online. Please contact Sergi Gonzàlez-Solís (<sergig@icc.ub.edu>) if you need login details.
Rare semileptonic decays provide some of the most sensitive probes of the Standard Model across both light- and heavy-quark sectors. I will begin with a brief pedagogical introduction to how flavor-changing processes connect kaon and B-meson physics, and why rare decays are especially powerful tools for testing the flavor structure of the Standard Model. I will then discuss how effective field theory methods can be used to separate short-distance weak dynamics from long-distance strong-interaction effects, with an emphasis on higher-order electroweak corrections that probe hadronic structure. The main focus will be on $K \to \pi \nu \bar{\nu}$ and $B \to X_s \nu \bar{\nu}$ decays, highlighting both the common theoretical ideas and the important differences between the light- and heavy-quark regimes.
We present a method for exploring the presence of noise, which may mimic systematic errors in experimental datasets, particularly when such errors introduce inconsistencies. The method is based on Padé approximants designed for Stieltjes functions, with extensions to holomorphic functions in the region covered by the data. It uses the known analytic properties of these functions to identify noise and systematically adjust data points that deviate from expected behavior, preserving the full information content of the dataset while maintaining physical and mathematical coherence. Its effectiveness and robustness are illustrated through simple examples, highlighting its advantages as a practical tool compared to conventional data-removal procedures.
TBA
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TBA