Wess-Zumino Inflation in Light of Planck

We discuss cosmological inflation in the minimal Wess-Zumino model with a single massive chiral supermultiplet. With suitable parameters and assuming a plausible initial condition at the start of the inflationary epoch, the model can yield scalar perturbations in the Cosmic Microwave Background (CMB) of the correct strength with a spectral index n_s ~ 0.96 and a tensor-to-scalar perturbation ratio r < 0.1, consistent with the Planck CMB data. We also discuss the possibility of topological inflation within the Wess-Zumino model, and the possibility of combining it with a seesaw model for neutrino masses. This would violate R-parity, but at such a low rate that the lightest supersymmetric particle would have a lifetime long enough to constitute the astrophysical cold dark matter.Comment: 11 pages, 3 figure

How do neural networks see depth in single images?

Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a publicly available test set such as the KITTI vision benchmark is often the main result of the article. While such an evaluation shows how well neural networks can estimate depth, it does not show how they do this. To the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take the MonoDepth network by Godard et al. and investigate what visual cues it exploits for depth estimation. We find that the network ignores the apparent size of known obstacles in favor of their vertical position in the image. Using the vertical position requires the camera pose to be known; however we find that MonoDepth only partially corrects for changes in camera pitch and roll and that these influence the estimated depth towards obstacles. We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so. In future work we will investigate whether these observations also apply to other neural networks for monocular depth estimation.Comment: Submitte

Gravitational Waves from a Pati-Salam Phase Transition

We analyse the gravitational wave and low energy signatures of a Pati-Salam phase transition. For a Pati-Salam scale of $M_{PS} \sim 10^5$ GeV, we find a stochastic power spectrum within reach of the next generation of ground-based interferometer experiments such as the Einstein Telescope, in parts of the parameter space. We study the lifetime of the proton in this model, as well as complementarity with low energy constraints including electroweak precision data, neutrino mass measurements, lepton flavour violation, and collider constraints.Comment: 26 pages, 7 figure