A Tale of Two Distances

There are two basic ways to measure physical distances in cosmology: One based on standard candles and one based on standard rulers. Comparing current data for each method allows us to rule out axion-photon mixing and dust-extinction as the sources of supernova dimming and generally protects the case for cosmic acceleration from attacks based on loss of photons. The combined data constrains the energy densities in a LCDM model to 0.19 < Omega_m < 0.32 and 0.47 < Omega_Lambda < 0.82 (at 2\sigma) without recourse to any further data sets. Future data will improve on these limits and allow us to place constraints on more exotic physics.Comment: 4 pages, 1 figure, uses moriond.sty. To be published in the Proceedings of the XXXIX Rencontres de Moriond 'Exploring the Universe

Inflationary preheating and primordial black holes

Preheating after inflation may over-produce primordial black holes (PBH's) in many regions of parameter space. As an example we study two-field models with a massless self-interacting inflaton, taking into account second order field and metric backreaction effects as spatial averages. We find that a complex quilt of parameter regions above the Gaussian PBH over-production threshold emerges due to the enhancement of curvature perturbations on all scales. It should be possible to constrain realistic models of inflation through PBH over-production although many issues, such as rescattering and non-Gaussianity, remain unsolved or unexplored.Comment: 7 pages, 6 figures. Fig 6 is added to show the weak dependence of the mass variance on the initial conditio

EDEN: Evolutionary Deep Networks for Efficient Machine Learning

Deep neural networks continue to show improved performance with increasing depth, an encouraging trend that implies an explosion in the possible permutations of network architectures and hyperparameters for which there is little intuitive guidance. To address this increasing complexity, we propose Evolutionary DEep Networks (EDEN), a computationally efficient neuro-evolutionary algorithm which interfaces to any deep neural network platform, such as TensorFlow. We show that EDEN evolves simple yet successful architectures built from embedding, 1D and 2D convolutional, max pooling and fully connected layers along with their hyperparameters. Evaluation of EDEN across seven image and sentiment classification datasets shows that it reliably finds good networks -- and in three cases achieves state-of-the-art results -- even on a single GPU, in just 6-24 hours. Our study provides a first attempt at applying neuro-evolution to the creation of 1D convolutional networks for sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video https://vimeo.com/23451009

Perturbative superluminal censorship and the null energy condition

We argue that ``effective'' superluminal travel, potentially caused by the tipping over of light cones in Einstein gravity, is always associated with violations of the null energy condition (NEC). This is most easily seen by working perturbatively around Minkowski spacetime, where we use linearized Einstein gravity to show that the NEC forces the light cones to contract (narrow). Given the NEC, the Shapiro time delay in any weak gravitational field is always a delay relative to the Minkowski background, and never an advance. Furthermore, any object travelling within the lightcones of the weak gravitational field is similarly delayed with respect to the minimum traversal time possible in the background Minkowski geometry.Comment: 5 pages. Uses AIP proceedings style (aipproc.sty). To appear in the Proceedings of the Eighth Canadian Conference on General Relativity and Relativistic Astrophysics. (McGill University, Montreal, June 1999). To be published by AIP Pres