activity
20112015
most citedOn Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

315 citations · 921 across the 7 of their papers we have counts for

collaborators

7 papers

astro-ph.CO2015★ 86 cited

Satellite galaxies in semi-analytic models of galaxy formation with sterile neutrino dark matter

Mark R. Lovell, Sownak Bose, Alexey Boyarsky +6

The sterile neutrino is a viable dark matter candidate that can be produced in the early Universe via non-equilibrium processes, and would therefore possess a highly non-thermal sp…

astro-ph.GA2014★ 45 cited

Creating mock catalogues of stellar haloes from cosmological simulations

Ben Lowing, Wenting Wang, Andrew Cooper +4

We present a new technique for creating mock catalogues of the individual stars that make up the accreted component of stellar haloes in cosmological simulations and show how the c…

astro-ph.CO2013★ 158 cited

Constraining the WDM Particle Mass with Milky Way Satellites

Rachel Kennedy, Carlos Frenk, Shaun Cole +1

Well-motivated particle physics theories predict the existence of particles (such as sterile neutrinos) which acquire non-negligible thermal velocities in the early universe. These…

astro-ph.CO2012★ 178 cited

Dark Matter Halo Merger Histories Beyond Cold Dark Matter: I - Methods and Application to Warm Dark Matter

Andrew J. Benson, Arya Farahi, Shaun Cole +6

We describe a methodology to accurately compute halo mass functions, progenitor mass functions, merger rates and merger trees in non-cold dark matter universes using a self-consist…

astro-ph.IM2011★ 80 cited

The BigBOSS Experiment

D. Schlegel, F. Abdalla, T. Abraham +138

BigBOSS is a Stage IV ground-based dark energy experiment to study baryon acoustic oscillations (BAO) and the growth of structure with a wide-area galaxy and quasar redshift survey…

astro-ph.IM2011★ 315 cited

On Machine-Learned Classification of Variable Stars with Sparse and Noisy Time-Series Data

Joseph W. Richards, Dan L. Starr, Nathaniel R. Butler +6

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for new…