IRC-Safe Graph Autoencoder for Unsupervised Anomaly Detection

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favorable properties, it also exhibits formidable sensitivity to non-QCD structures.

CP Violation in Higgs-Gauge Interactions: From Tabletop Experiments to the LHC

We investigate the interplay between the high- and low-energy phenomenology of CP-violating interactions of the Higgs boson with gauge bosons. For this purpose, we use an effective field theory approach and consider all dimension-six operators arising in so-called universal theories. We compute their loop-induced contributions to electric dipole moments and the CP asymmetry in B→X_{s}γ and compare the resulting current and prospective constraints to the projected sensitivity of the LHC. Low-energy measurements are shown to generally have a far stronger constraining power, which results in highly correlated allowed regions in coupling space-a distinctive pattern that could be probed at the high-luminosity LHC.