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X-WR-CALNAME:Instituto de Física Teórica
X-ORIGINAL-URL:https://www.ift.uam-csic.es
X-WR-CALDESC:Events for Instituto de Física Teórica
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TZID:Europe/Madrid
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DTSTART;TZID=Europe/Madrid:20260921T110000
DTEND;TZID=Europe/Madrid:20260921T130000
DTSTAMP:20260921T021012
CREATED:20260908T111916Z
LAST-MODIFIED:20260918T074317Z
UID:25541-1789988400-1789995600@www.ift.uam-csic.es
SUMMARY:PhD Defense by Indira Ocampo Justiniano: A machine learning approach for unraveling the nature of dark matter and dark energy
DESCRIPTION:Title: A machine learning approach for unraveling the nature of dark matter and dark energy \nVenue & Time: Orange Room / 11:00-13:00 \n\nAbstract: Cosmology nowadays is benefiting from an exponential increase of data\, transforming into a precision science. This will help us understand better two of the most important open issues in theoretical physics\, namely understanding the nature of dark matter and dark energy. However\, the vast quantity and quality of the data collected from present and upcoming cosmological surveys has become so demanding\, that sophisticated computational tools are required. As a result\, machine learning methods are being implemented for data analysis and model constraints with the hope of alleviating tensions in the parameters inferred from different cosmological probes. A significant challenge remains in the complexity of these models: as architectures become more accurate\, this usually comes with the trade-off of their interpretability. This can lead to a lack of transparency and trustworthiness in a field that demands validation based on physical laws. The research presented in this thesis focuses on different architectures based on neural networks to search for signatures of physics beyond ΛCDM\, with a particular emphasis on interpretability tools that allow extracting the most informative data for the model’s predictions. \nSupervisor: Savvas Nesseris\nA small reception will take place afterwards on the terrace of IMDEA around 13:00.
URL:https://www.ift.uam-csic.es/event/phd-defense-by-indira-ocampo-justiniano-a-machine-learning-approach-for-unraveling-the-nature-of-dark-matter-and-dark-energy/
LOCATION:Orange Room\, Instituto de Física Teórica (IFT) -C. Nicolás Cabrera\, 13-15\, Fuencarral-El Pardo\, Madrid\, 28049\, Spain
CATEGORIES:PhD Dissertation,Training
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DTSTART;TZID=Europe/Madrid:20260921T150000
DTEND;TZID=Europe/Madrid:20260921T160000
DTSTAMP:20260921T021012
CREATED:20260821T101946Z
LAST-MODIFIED:20260918T144926Z
UID:25494-1790002800-1790006400@www.ift.uam-csic.es
SUMMARY:Seminar by Georg Weiglein (DESY): "Exploring the Higgs potential and the evolution of the early universe"
DESCRIPTION:Title: Exploring the Higgs potential and the evolution of the early universe \nSpeaker: Georg Weiglein (DESY) \nVenue&Time: Orange Room | 15:00 \nAbstract: The Higgs potential is of central importance for addressing fundamental open questions in our understanding of the dynamics of electroweak symmetry breaking\, its connection to the electroweak phase transition in the early universe and for possible explanations for the observed imbalance between matter and anti-matter in the universe. However\, up to now neither the shape of the Higgs potential that is realised in nature is known nor the underlying physics that actually gives rise to the Higgs potential. The present experimental results from the LHC related to the Higgs potential are confronted with precise theory predictions\, and future prospects for the exploration of the HIggs potential are highlighted. The theoretical uncertainties in predicting the thermal evolution of the early universe and possible gravitational wave signals arising from the electroweak phase transition are discussed.
URL:https://www.ift.uam-csic.es/event/seminar-georg-weiglein-desy/
LOCATION:Orange 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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