Ultraviolet behavior of 6D supersymmetric Yang-Mills theories and harmonic superspace

We revisit the issue of higher-dimensional counterterms for the N=(1,1) supersymmetric Yang-Mills (SYM) theory in six dimensions using the off-shell N=(1,0) and on-shell N=(1,1) harmonic superspace approaches. The second approach is developed in full generality and used to solve, for the first time, the N=(1,1) SYM constraints in terms of N=(1,0) superfields. This provides a convenient tool to write explicit expressions for the candidate counterterms and other N=(1,1) invariants and may be conducive to proving non-renormalization theorems needed to explain the absence of certain logarithmic divergences in higher-loop contributions to scattering amplitudes in N=(1,1) SYM.Comment: 55 pages, published version in JHE

Gravity Resonance Spectroscopy and Einstein-Cartan Gravity

The qBounce experiment offers a new way of looking at gravitation based on quantum interference. An ultracold neutron is reflected in well-defined quantum states in the gravity potential of the Earth by a mirror, which allows to apply the concept of gravity resonance spectroscopy (GRS). This experiment with neutrons gives access to all gravity parameters as the dependences on distance, mass, curvature, energy-momentum as well as on torsion. Here, we concentrate on torsion.Comment: Contributed to the 11th Patras Workshop on Axions, WIMPs and WISPs, Zaragoza, June 22 to 26, 2015, 6 pages, 4 figure

Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators

This paper presents a novel approach for constructing neural networks which model charged particle beam dynamics. In our approach, the Taylor maps arising in the representation of dynamics are mapped onto the weights of a polynomial neural network. The resulting network approximates the dynamical system with perfect accuracy prior to training and provides a possibility to tune the network weights on additional experimental data. We propose a symplectic regularization approach for such polynomial neural networks that always restricts the trained model to Hamiltonian systems and significantly improves the training procedure. The proposed networks can be used for beam dynamics simulations or for fine-tuning of beam optics models with experimental data. The structure of the network allows for the modeling of large accelerators with a large number of magnets. We demonstrate our approach on the examples of the existing PETRA III and the planned PETRA IV storage rings at DESY