Deep-learned Top Tagging with a Lorentz Layer

We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.Comment: v3: minor revisions following SciPost referee report

Global Fits for New Physics at the LHC and Beyond

We study physics beyond the Standard Model with state–of–the–art global fits of both UV-complete models like supersymmetry and the more general effective field theories (EFTs). The gamma-ray excess from the galactic center measured by Fermi–LAT can be interpreted as a dark matter signature in the minimal supersymmetric model. Using the SFitter framework we identify different annihilation channels with a dark matter mass up to 300 GeV yielding the measured spectrum. Strong constraints from direct detection experiments and relic density rule out large regions of the parameter space, favoring a pseudoscalar mediator. In the next– to–minimal supersymmetric model the additional singlet allows efficient annihilation of dark matter particles below 60 GeV via a light pseudoscalar. We connect the resulting solutions to the GC excess with a large invisible Higgs branching ratio in reach of the LHC. Finally we use the EFT framework to constrain higher-dimensional operators from the Higgs and the electroweak gauge sector. Our bounds on triple gauge–boson couplings from LHC di– boson channels are several times stronger than those obtained from LEP data. The combination of Higgs measurements and triple gauge vertices leads to a significant improvement in the entire set of operators

Invisible Higgs Decays to Hooperons in the NMSSM

The galactic center excess of gamma ray photons can be naturally explained by light Majorana fermions in combination with a pseudoscalar mediator. The NMSSM provides exactly these ingredients. We show that for neutralinos with a significant singlino component the galactic center excess can be linked to invisible decays of the Standard-Model-like Higgs at the LHC. We find predictions for invisible Higgs branching ratios in excess of 50 percent, easily accessible at the LHC. Constraining the NMSSM through GUT-scale boundary conditions only slightly affects this expectation. Our results complement earlier NMSSM studies of the galactic center excess, which link it to heavy Higgs searches at the LHC.Comment: 23 pages, 24 figures; v2: references adde

Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods

The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Traditionally, many analyses unfold detector effects using transfer-matrix--based algorithms, which are well established in low-dimensional unfolding. They yield an unfolded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distributions, together with their uncertainties and correlations, for the transfer-matrix--based unfolding. The algorithm is first validated on a toy model and then applied to pseudo-data for the $pp\rightarrow Z\gamma \gamma$ process. In both examples the performance is compared to the single-event unfolding of the Machine-Learning--based Iterative cINN unfolding (IcINN).Comment: 22 pages, 11 figure