Probing the flavor structure of New Physics models with precision observablesSmolkovič, Aleks (Avtor) Košnik, Nejc (Mentor) beyond the Standard Modelflavor physicsCP violationoptimized observablesdark matterleptoquarkmachine learning in particle physicsThe Standard Model of particle physics is reviewed with the emphasis on the flavor aspects of the theory, together with its many shortcomings. The enhancement of CP asymmetries in $B \to K \mu \mu$ due to the effects of narrow charmonia is studied and proposed as a viable probe of the CP nature of the physics beyond the Standard Model, which might be entering in $b \to s \mu \mu$ transitions. Such direct CP asymmetries offer probes that are highly complementary to the observables sensitive to lepton flavor universality violation, which show promising experimental deviations from the Standard Model predictions. Optimized probes of the CP nature of the top quark Yukawa coupling are studied both in single top quark as well as top-anti top quark pair associated production with the Higgs boson at proton-proton colliders. Estimated bounds from using such probes are given for the High Luminosity and High Energy Large Hadron Collider, as well as the hadronic Future Circular Collider. A connection between flavor anomalies and dark matter is attempted in the context of leptoquark models and a real scalar singlet dark matter candidate. A high-mass dark matter regime is pointed out as viable by considering constraints from relic abundance, direct, indirect, and collider searches, as well as the scalar potential stability. The combination of these constraints offers a novel bound on the leptoquark mass. An unsupervised machine learning technique based on variational autoencoders is presented as a powerful tool that could help at characterizing difficult signal signatures at colliders. Such an approach offers a highly competitive classification performance, and shows promising results for the characterization of signal properties by using the compressed representation of data in the latent space.20212021-09-22 08:15:21Doktorsko delo/naloga131039sl