Zooniverse

Abstract:

JINGLE is a new JCMT legacy survey designed to systematically study the cold interstellar medium of galaxies in the local Universe. As part of the survey we perform 850 µm continuum measurements with SCUBA-2 for a representative sample of 193 Herschel-selected galaxies with M* > 109 M⊙, as well as integrated CO(2–1) line fluxes with RxA3m for a subset of 90 of these galaxies. The sample is selected from fields covered by the Herschel-ATLAS survey that are also targeted by the MaNGA optical integral-field spectroscopic survey. The new JCMT observations combined with the multiwavelength ancillary data will allow for the robust characterization of the properties of dust in the nearby Universe, and the benchmarking of scaling relations between dust, gas, and global galaxy properties. In this paper we give an overview of the survey objectives and details about the sample selection and JCMT observations, present a consistent 30-band UV-to-FIR photometric catalogue with derived properties, and introduce the JINGLE Main Data Release. Science highlights include the non-linearity of the relation between 850 µm luminosity and CO line luminosity (log LCO(2–1) =  1.372 logL850–1.376), and the serendipitous discovery of candidate z > 6 galaxies.

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.

Abstract:

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme we increase the classification rate nearly 5-fold, classifying 226,124 galaxies in 92 days of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7% accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of nonparametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine, and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210,803 galaxies in just 32 days of GZ2 project time with 93.1% accuracy. As the Random Forest algorithm requires a minimal amount of computation cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large scale surveys.