We perform a comprehensive study of the allowed parameter space of the Two Higgs Doublet Model of Type II (2HDM-II). Using the theoretical framework flavio we combine the most recent flavour, collider and electroweak precision observables with theoretical constraints to obtain bounds on the mass spectrum of the theory. In particular we find that the 2HDM-II fits the data slightly better than the Standard Model (SM) with best fit values of the heavy Higgs masses around 2 TeV and a value of tan β ≈ 4. Moreover, we conclude that the wrong-sign limit is disfavoured by Higgs signal strengths and excluded by the global fit by more than five standard deviations and potential deviations from the alignment limit can only be tiny. Finally we test the consequences of our study on electroweak baryogenesis via the program package BSMPT and we find that the allowed parameter space strongly discourages a strong first order phase transition within the 2HDM-II.

We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously reconstructing edge features and node features. Focusing on latent space based discriminators, we find that such setups provide a promising avenue to isolate new physics and competing SM signatures from sensitivity-limiting QCD jet contributions. We demonstrate the flexibility and broad applicability of this approach using examples of W bosons, top quarks, and exotic hadronically-decaying exotic scalar bosons.

We study the intersection of flavour and collider physics for Two-Higgs-Doublet models of Type I and II. Drawing from the flavour precision-LHC exotics search complementarity, we also provide a projection of the future sensitivity that can be achieved in light of currently available analyses. On the one hand, we find that the parameter space of the 2HDM can be explored significantly further with more data from the LHC with some complementarity with flavour physics. On the other hand, flavour physics results alongside their projections remain powerful tools to constrain the model space in regions where direct sensitivity to new states via exotics searches is lost. Our results further highlight the recently observed flavour physics anomalies as important drivers of new physics searches in the future; we also touch on implications for a strong first order electroweak phase transition.

We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional rectangular cut-based differential distribution analyses. In this study, we focus on a SMEFT analysis of pp → $$ t\overline{t} $$ t t ¯ production, including top decays, where the linear effective field deformation is parametrised by thirteen independent Wilson coefficients. The application of GNNs allows us to condense the multidimensional phase space information available for the discrimination of BSM effects from the SM expectation by considering all available final state correlations directly. The number of contributing new physics couplings very quickly leads to statistical limitations when the GNN output is directly employed as an EFT discrimination tool. However, a selection based on minimising the SM contribution enhances the fit’s sensitivity when reflected as a (non-rectangular) selection on the inclusive data samples that are typically employed when looking for non-resonant deviations from the SM by means of differential distributions.

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.