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DTSTART;TZID=Europe/Madrid:20260918T110000
DTEND;TZID=Europe/Madrid:20260918T133000
DTSTAMP:20260921T224644
CREATED:20260908T110406Z
LAST-MODIFIED:20260918T114325Z
UID:25536-1789729200-1789738200@www.ift.uam-csic.es
SUMMARY:PhD Defense by David Alonso-González: Into The Dark: Novel and Complementary Searches in Hidden Sectors
DESCRIPTION:Title: Into The Dark: Novel and Complementary Searches in Hidden Sectors\n\nVenue & Time: Blue Room / 11:00-13:30\n\nAbstract: This thesis explores weakly coupled extensions of the Standard Model\, focusing on hidden sectors that may evade conventional experimental searches while addressing some of the main open questions in particle physics\, such as the nature of dark matter and the origin of neutrino masses. The work is organized around three complementary case studies involving a thermal dark matter candidate connected to the Standard Model through a vector portal\, axionlike particles produced in core-collapse supernovae and their possible detection at terrestrial neutrino experiments\, and the interplay between dark matter direct detection experiments and spallation source facilities to constrain the properties of sterile neutrinos. These three works emphasize the importance of combining different experimental strategies to constrain or reconstruct new weakly coupled physics scenarios.
URL:https://www.ift.uam-csic.es/event/phd-defense-by-david-alonso-gonzalez-into-the-dark-novel-and-complementary-searches-in-hidden-sectors/
LOCATION:Blue 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:20260921T110000
DTEND;TZID=Europe/Madrid:20260921T130000
DTSTAMP:20260921T224644
CREATED:20260908T111916Z
LAST-MODIFIED:20260921T093237Z
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 \n\nA small reception will take place afterwards on the terrace of IMDEA around 13:00. \n  \n 
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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