Bondi or not Bondi: the impact of resolution on accretion and drag force modelling for supermassive black holes

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 478:1 (2018) 995-1016

Authors:

RS Beckmann, A Slyz, J Devriendt

KiDS-i-800: comparing weak gravitational lensing measurements from same-sky surveys

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 477:4 (2018) 4285-4307

Authors:

A Amon, C Heymans, D Klaes, T Erben, C Blake, H Hildebrandt, H Hoekstra, K Kuijken, L Miller, CB Morrison, A Choi, JTA de Jong, K Glazebrook, N Irisarri, B Joachimi, S Joudaki, A Kannawadi, C Lidman, N Napolitano, D Parkinson, P Schneider, E van Uitert, M Viola, C Wolf

Emergent dark energy from dark matter

PHYSICAL REVIEW D 97:12 (2018) ARTN 121301

Authors:

Takeshi Kobayashi, Pedro G Ferreira

Neutrino masses and beyond-Lambda CDM cosmology with LSST and future CMB experiments

PHYSICAL REVIEW D 97:12 (2018) ARTN 123544

Authors:

Siddharth Mishra-Sharma, David Alonso, Joanna Dunkley

Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project.

Scientific data (2018)

Authors:

FM Jones, C Allen, C Arteta, J Arthur, C Black, LM Emmerson, R Freeman, G Hines, CJ Lintott, Z Macháčková, G Miller, R Simpson, C Southwell, HR Torsey, A Zisserman, Tom Hart

Abstract:

Automated time-lapse cameras can facilitate reliable and consistent monitoring of wild animal populations. In this report, data from 73,802 images taken by 15 different Penguin Watch cameras are presented, capturing the dynamics of penguin (Spheniscidae; Pygoscelis spp.) breeding colonies across the Antarctic Peninsula, South Shetland Islands and South Georgia (03/2012 to 01/2014). Citizen science provides a means by which large and otherwise intractable photographic data sets can be processed, and here we describe the methodology associated with the Zooniverse project Penguin Watch, and provide validation of the method. We present anonymised volunteer classifications for the 73,802 images, alongside the associated metadata (including date/time and temperature information). In addition to the benefits for ecological monitoring, such as easy detection of animal attendance patterns, this type of annotated time-lapse imagery can be employed as a training tool for machine learning algorithms to automate data extraction, and we encourage the use of this data set for computer vision development.