Exotic Gravitational Wave Signatures from Simultaneous Phase Transitions

We demonstrate that the relic gravitational wave background from a multi-step phase transition may deviate from the simple sum of the single spectra, for phase transitions with similar nucleation temperatures $T_N$. We demonstrate that the temperature range $\Delta T$ between the volume fractions $f(T)=0.1$ and $f(T)=0.9$ occupied by the vacuum bubbles can span $\sim 20$ GeV. This allows for a situation in which phase transitions overlap, such that the later bubbles may nucleate both in high temperature and intermediate temperature phases. Such scenarios may lead to more exotic gravitational wave spectra, which cannot be fitted that of a consecutive PTs. We demonstrate this explicitly in the singlet extension of the Standard Model. Finally, we comment on potential additional effects due to the more exotic dynamics of overlapping phase transitions.Comment: 25 pages, 7 figures. Published versio

Reheating with a composite Higgs boson

The flatness of the inflaton potential and lightness of the Higgs boson could have the common origin of the breaking of a global symmetry. This scenario provides a unified framework of Goldstone inflation and composite Higgs models, where the inflaton and the Higgs particle both have a pseudo-Goldstone boson nature. The inflaton reheats the Universe via decays to the Higgs and subsequent secondzary production of other SM particles via the top and massive vector bosons. We find that inflationary predictions and perturbative reheating conditions are consistent with cosmic microwave background data for sub-Planckian values of the fields, as well as opening up the possibility of inflation at the TeV scale. We explore this exciting possibility, leading to an interplay between collider data cosmological constraints

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

Surrogate modeling of RF circuit blocks

Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices. Within the European Marie Curie project O-MOORE-NICE! (Operational Model Order Reduction for Nanoscale IC Electronics) we aim to investigate the feasibility of the surrogate modeling approach for entire RF circuit blocks. This paper presents an overview about the surrogate model type selection problem for low noise amplifier modeling

A general weak nonlinearity model for LNAs

This paper presents a general weak nonlinearity model that can be used to model, analyze and describe the distortion behavior of various low noise amplifier topologies in both narrowband and wideband applications. Represented by compact closed-form expressions our model can be easily utilized by both circuit designers and LNA design automation algorithms.\ud Simulations for three LNA topologies at different operating conditions show that the model describes IM components with an error lower than 0.1% and a one order of magnitude faster response time. The model also indicates that for narrowband IM2@w1-w2 all the nonlinear capacitances can be neglected while for narrowband IM3 the nonlinear capacitances at the drainterminal can be neglected