Generative deep fields : arbitrarily sized, random synthetic astronomical images through deep learning

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond.Peer reviewe

Application of a panel method to wake-vortex/wing interaction and comparison with experimental data

The ability of the Vortex Separation AEROdynamics (VSAERO) program to calculate aerodynamic loads on wings due to interaction with free vortices was studied. The loads were calculated for various positions of a downstream following wing relative to an upstream vortex-generating wing. Calculated vortex-induced span loads, rolling-moment coefficients, and lift coefficients on the following wing were compared with experimental results of McMillan et al. and El-Ramly et al. Comparisons of calculated and experimental vortex tangential velocities were also made

MARINE RESERVES WITH ENDOGENOUS PORTS: EMPIRICAL BIOECONOMICS OF THE CALIFORNIA SEA URCHIN FISHERY

Marine reserves are gaining substantial public support as tools for commercial fisheries management Harvest sector responses will influence policy performance, yet biological studies often depict harvester behavior as spread uniformly over fishing grounds and unresponsive to economic opportunities. Previous bioeconomic analyses show that these behavioral assumptions are inconsistent with empirical data and, more importantly, lead to overly optimistic predictions about harvest gains from reserves. This paper adds another layer of behavioral realism to the bioeconomics of marine reserves by endogenizing fisher home port choices with a partial adjustment share model. Estimated with Seemingly Unrelated Regression over monthly data, this approach allows simulation of both short- and long-run behavioral response to changes induced by marine reserve formation. The findings cast further doubt on the notion that marine reserves generate long-run harvest benefits.Resource /Energy Economics and Policy,

ANALYSIS OF A SPATIAL ROTATION PLAN FOR THE TULE LAKE NATIONAL WILDLIFE REFUGE

This paper examines the joint agro-wildfowl regulation of the Tule Lake National Wildlife Refuge in California. The area is jointly managed by the Bureau of Reclamation for both farming and wildfowl benefits. Production in both sectors has been declining recently, in farming due to nematode and soil pathogen buildup and in wildfowl production due to climax vegetation choking the lake. A novel spatial rotation plan has surfaced to solve both problems. We develop a simple model of the rotation option to identify critical variables and then we estimate some of these using data on lease bids.Resource /Energy Economics and Policy,