activity
20062013
most citedThe WiggleZ Dark Energy Survey: the transition to large-scale cosmic homogeneity

212 citations · 793 across the 17 of their papers we have counts for

collaborators

17 papers

astro-ph.CO2013★ 33 cited

Using the topology of large-scale structure in the WiggleZ Dark Energy Survey as a cosmological standard ruler

Chris Blake, J. Berian James, Gregory B. Poole

We present new and accurate measurements of the cosmic distance-redshift relation, spanning 0.2 < z < 1, using the topology of large-scale structure as a cosmological standard rule…

astro-ph.CO2012★ 212 cited

The WiggleZ Dark Energy Survey: the transition to large-scale cosmic homogeneity

Morag Scrimgeour, Tamara Davis, Chris Blake +26

We have made the largest-volume measurement to date of the transition to large-scale homogeneity in the distribution of galaxies. We use the WiggleZ survey, a spectroscopic survey…

astro-ph.CO2011★ 7 cited

Physics of non-Gaussian fields and the cosmological genus statistic

J. Berian James

We report a technique to calculate the impact of distinct physical processes inducing non-Gaussianity on the cosmological density field. A natural decomposition of the cosmic genus…

astro-ph.HE2011★ 74 cited

Clustering between high-mass X-ray binaries and OB associations in the Milky Way

Arash Bodaghee, John A. Tomsick, Jerome Rodriguez +1

We present the first direct measurement of the spatial cross-correlation function of high-mass X-ray binaries (HMXBs) and active OB star-forming complexes in the Milky Way. This re…

astro-ph.CO2011★ 49 cited

Clipping the Cosmos: The Bias and Bispectrum of Large Scale Structure

Fergus Simpson, J. Berian James, Alan F. Heavens +1

A large fraction of the information collected by cosmological surveys is simply discarded to avoid lengthscales which are difficult to model theoretically. We introduce a new techn…

astro-ph.IM2011★ 65 cited

Active Learning to Overcome Sample Selection Bias: Application to Photometric Variable Star Classification

Joseph W. Richards, Dan L. Starr, Henrik Brink +6

Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in l…