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DTSTART;TZID=Europe/Madrid:20250929T090000
DTEND;TZID=Europe/Madrid:20251002T183000
DTSTAMP:20260721T101526
CREATED:20250922T115745Z
LAST-MODIFIED:20251218T085639Z
UID:22872-1759136400-1759429800@www.ift.uam-csic.es
SUMMARY:LHC Working Group on Forward Physics and Diffraction
DESCRIPTION:The September 29-October 2  2025 four-day workshop in Madrid of the LHC Physics Centre at CERN (LPCC) Forward Physics Working Group provides a forum at CERN for experimental collaborations and theorists to meet and exchange latest results across all topics of forward physics at the LHC. \nThe workshop will take place at the Institute of Theoretical Physics (Campus de Cantoblanco 28049 Madrid Spain) and we will be hosted by Prof. Agustin Sabio Vera. To come to IFT\, you can take Line C-4 (Colmenar Viejo or Alcobendas/San Sebastián de los Reyes)\, that takes you to the University (Cantoblanco-Universidad Station). You can pick it up in Atocha (main station in the Centre/South of Madrid)\, Nuevos Ministerios (Paseo de la Castellana) and Chamartín (main station in the North). After Chamartín\, the second station is Cantoblanco-Universidad\, where you should exit. \nMore info.
URL:https://www.ift.uam-csic.es/event/lhc-working-group-on-forward-physics-and-diffraction/
LOCATION:Blue Room\, Instituto de Física Teórica (IFT) - C. Nicolás Cabrera\, 13-15\, Fuencarral-El Pardo\, Madrid\, 28049\,\, Spain
CATEGORIES:Scientific activities,Seminars
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DTSTART;TZID=Europe/Madrid:20251002T150000
DTEND;TZID=Europe/Madrid:20251002T160000
DTSTAMP:20260721T101526
CREATED:20250613T073446Z
LAST-MODIFIED:20251218T085638Z
UID:18492-1759417200-1759420800@www.ift.uam-csic.es
SUMMARY:Fast inference for slow likelihoods with probabilistic machine-learning
DESCRIPTION:Speaker: Jesús Torrado from IEM-CSIC\n\n\n\n\nVenue&Time: Red Room / 3:00 PM\n\n\n\nAbstract: Inference for slow likelihoods can require weeks or months\, if possible at all\, with traditional Monte Carlo samplers. This would be the case\, for example\, when fitting expensive non-linear matter spectrum models\, or when characterizing individual long-duration GW events. I will introduce a fast machine-learning Bayesian inference algorithm for general non-Gaussian posteriors with a moderate number of parameters. I will present a pedagogical discussion of some general aspects of it\, such as dealing with the curse of dimensionality\, characterizing a region of interest\, or parallelising active learning. I will show that the total number of expensive likelihood evaluations can be reduced by at least two orders of magnitude compared to traditional Monte Carlo methods\, at low overhead costs and no pre-training. I will demonstrate its performance on a couple of real cosmological and GW problems.
URL:https://www.ift.uam-csic.es/event/tba-4/
LOCATION:IFT Seminar Room/Red Room\, Instituto de Física Teórica (IFT) -C. Nicolás Cabrera\, 13-15\, Fuencarral-El Pardo\, Madrid\, 28049\,\, Spain
CATEGORIES:Scientific activities,Seminars
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