BEGIN:VCALENDAR VERSION:2.0 PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN BEGIN:VEVENT SUMMARY:Dark Matter and Machine Learning in Multi-Higgs Doublet Models DTSTART:20251113T150000Z DTEND:20251113T170000Z DTSTAMP:20260619T173146Z UID:82f5747c-d259-4d6b-a912-376e9cc4d40b SEQUENCE:2 CREATED:20251111T094608Z DESCRIPTION:This thesis investigates extensions of the scalar sector of th e Standard Model (SM)\, in particular models with N Higgs Doublets (NHDMs) \, applied to Dark Matter (DM) and new sources of CP violation. It combine s the study of Multi-Higgs models with modern Machine Learning (ML) techni ques for the exploration of high-dimensional parameter spaces. This approa ch is broadly applicable to any BSM scenario and offers a versatile path t o uncover new physics with concrete experimental implications.First\, we s tudy the Z2xZ2 Three Higgs Doublet Model (3HDM) with two stable inert scal ars\, leading to a multi-component DM framework. A rigorous analysis allow s us to derive new conditions for the global minimum of the potential and identify regions where both DM candidates contribute comparably to the obs erved relic abundance. We impose all theoretical and experimental constrai nts and explore their interplay and complementarity across the full parame ter space. This model displays a rich phenomenology inaccessible to minima l approaches\, such as the Inert Doublet Model (IDM)\, thereby highlightin g the predictive power of scalar sector extensions.Next\, we analyse the C P-violating Z2xZ2 C3HDM\, which features large pseudoscalar Yukawa couplin gs that are tightly constrained and challenging to probe using conventiona l methods. To overcome this\, we implement an ML-based scanning algorithm combining an Evolutionary Strategy with a Novelty Reward mechanism. This p rocedure accelerates the identification of new viable regions and uncovers clear predictions\, particularly in the Higgs couplings to the top and bo ttom quarks. LAST-MODIFIED:20251111T094621Z LOCATION:Sala P3 (Piso 1 do Pavilhão de Matemática) do IST URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/dark-matter-and-machine-le arning-in-multi-higgs-doublet-models/ X-ALT-DESC;FMTTYPE=text/html:

This thesis investi gates extensions of the scalar sector of the Standard Model (SM)\, in part icular models with N Higgs Doublets (NHDMs)\, applied to Dark Matter (DM) and new sources of CP violation. It combines the study of Multi-Higgs mode ls with modern Machine Learning (ML) techniques for the exploration of hig h-dimensional parameter spaces. This approach is broadly applicable to any BSM scenario and offers a versatile path to uncover new physics with conc rete experimental implications.

Fir st\, we study the Z2xZ2 Three Higgs Doublet Model (3HDM) with two stable i nert scalars\, leading to a multi-component DM framework. A rigorous analy sis allows us to derive new conditions for the global minimum of the poten tial and identify regions where both DM candidates contribute comparably t o the observed relic abundance. We impose all theoretical and experimental constraints and explore their interplay and complementarity across the fu ll parameter space. This model displays a rich phenomenology inaccessible to minimal approaches\, such as the Inert Doublet Model (IDM)\, thereby hi ghlighting the predictive power of scalar sector extensions.

Next\, we analyse the CP-violating Z2xZ2 C3HDM\, which features large pseudoscalar Yukawa couplings that are tightly const rained and challenging to probe using conventional methods. To overcome th is\, we implement an ML-based scanning algorithm combining an Evolutionary Strategy with a Novelty Reward mechanism. This procedure accelerates the identification of new viable regions and uncovers clear predictions\, part icularly in the Higgs couplings to the top and bottom quarks.

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