Rapid Star Formation in the Presence of Active Galactic Nuclei

Recent observations reveal galaxies in the early Universe (2<z<6.4) with large reservoirs of molecular gas and extreme star formation rates. For a very large range of sources, a tight relationship exists between star formation rate and the luminosity of the HCN J=1-0 spectral line, but sources at redshifts of z~2 and beyond do not follow this trend. The deficit in HCN is conventionally explained by an excess of infrared (IR) radiation due to active galactic nuclei (AGN). We show in this letter not only that the presence of AGN cannot account for the excess of IR over molecular luminosity, but also that the observed abundance of HCN is in fact consistent with a population of stars forming from near-primordial gas.Comment: 4 pages, 1 figure. Accepted by the Astrophysical Journal Letter

Morphology in the Era of Large Surveys

The study of galaxies has changed dramatically over the past few decades with the advent of large-scale astronomical surveys. These large collaborative efforts have made available high-quality imaging and spectroscopy of hundreds of thousands of systems, providing a body of observations which has significantly enhanced our understanding not only of cosmology and large-scale structure in the universe but also of the astrophysics of galaxy formation and evolution. Throughout these changes, one thing that has remained constant is the role of galaxy morphology as a clue to understanding galaxies. But obtaining morphologies for large numbers of galaxies is challenging; this topic, "Morphology in the era of large surveys", was the subject of a recent discussion meeting at the Royal Astronomical Society, and this "Astronomy and Geophysics" article is a report on that meeting.Comment: Meeting Report article published in the October 2013 issue of the Royal Astronomical Society journal Astronomy and Geophysics. 4 page pdf with colour image

The Ultraviolet Attenuation Law in Backlit Spiral Galaxies

(Abridged) The effective extinction law (attenuation behavior) in galaxies in the emitted ultraviolet is well known only for actively star-forming objects and combines effects of the grain properties, fine structure in the dust distribution, and relative distributions of stars and dust. We use GALEX, XMM Optical Monitor, and HST data to explore the UV attenuation in the outer parts of spiral disks which are backlit by other UV-bright galaxies, starting with candidates provided by Galaxy Zoo participants. Our analysis incorporates galaxy symmetry, using non-overlapping regions of each galaxy to derive error estimates on the attenuation measurements. The entire sample has an attenuation law close to the Calzetti et al. (1994) form; the UV slope for the overall sample is substantially shallower than found by Wild et al. (2011), a reasonable match to the more distant galaxies in our sample but not to the weighted combination including NGC 2207. The nearby, bright spiral NGC 2207 alone gives accuracy almost equal to the rest of our sample, and its outer arms have a very low level of foreground starlight. This "grey" law can be produced from the distribution of dust alone, without a necessary contribution from differential escape of stars from dense clouds. The extrapolation needed to compare attenution between backlit galaxies at moderate redshifts, and local systems from SDSS data, is mild enough to allow use of galaxy overlaps to trace the cosmic history of dust. For NGC 2207, the covering factor of clouds with small optical attenuation becomes a dominant factor farther into the ultraviolet, which opens the possibility that widespread diffuse dust dominates over dust in star-forming regions deep into the ultraviolet. Comparison with published radiative-transfer models indicates that the role of dust clumping dominates over differences in grain populations, at this spatial resolution.Comment: In press, Astronomical Journa

Characterising Volunteers' Task Execution Patterns Across Projects on Multi-Project Citizen Science Platforms

Citizen science projects engage people in activities that are part of a scientific research effort. On multi-project citizen science platforms, scientists can create projects consisting of tasks. Volunteers, in turn, participate in executing the project's tasks. Such type of platforms seeks to connect volunteers and scientists' projects, adding value to both. However, little is known about volunteer's cross-project engagement patterns and the benefits of such patterns for scientists and volunteers. This work proposes a Goal, Question, and Metric (GQM) approach to analyse volunteers' cross-project task execution patterns and employs the Semiotic Inspection Method (SIM) to analyse the communicability of the platform's cross-project features. In doing so, it investigates what are the features of platforms to foster volunteers' cross-project engagement, to what extent multi-project platforms facilitate the attraction of volunteers to perform tasks in new projects, and to what extent multi-project participation increases engagement on the platforms. Results from analyses on real platforms show that volunteers tend to explore multiple projects, but they perform tasks regularly in just a few of them; few projects attract much attention from volunteers; volunteers recruited from other projects on the platform tend to get more engaged than those recruited outside the platform. System inspection shows that platforms still lack personalised and explainable recommendations of projects and tasks. The findings are translated into useful claims about how to design and manage multi-project platforms.Comment: XVIII Brazilian Symposium on Human Factors in Computing Systems (IHC'19), October 21-25, 2019, Vit\'oria, ES, Brazi

Galaxy Zoo: Morphological Classification and Citizen Science

We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning and Data Mining for Astronom