Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies

‘The definitive version is available at www3.interscience.wiley.com '. Copyright Royal Astronomical Society. DOI: 10.1111/j.1365-2966.2009.15383.xWe investigate a class of rapidly growing emission line galaxies, known as 'Green Peas', first noted by volunteers in the Galaxy Zoo project because of their peculiar bright green colour and small size, unresolved in Sloan Digital Sky Survey imaging. Their appearance is due to very strong optical emission lines, namely [O iii]λ5007 Å, with an unusually large equivalent width of up to ∼1000 Å. We discuss a well-defined sample of 251 colour-selected objects, most of which are strongly star forming, although there are some active galactic nuclei interlopers including eight newly discovered narrow-line Seyfert 1 galaxies. The star-forming Peas are low-mass galaxies (M∼ 108.5–1010 M⊙) with high star formation rates (∼10 M⊙ yr−1) , low metallicities (log[O/H]+ 12 ∼ 8.7) and low reddening [ E(B−V) ≤ 0.25 ] and they reside in low-density environments. They have some of the highest specific star formation rates (up to ∼10−8 yr−1 ) seen in the local Universe, yielding doubling times for their stellar mass of hundreds of Myr. The few star-forming Peas with Hubble Space Telescope imaging appear to have several clumps of bright star-forming regions and low surface density features that may indicate recent or ongoing mergers. The Peas are similar in size, mass, luminosity and metallicity to luminous blue compact galaxies. They are also similar to high-redshift ultraviolet-luminous galaxies, e.g. Lyman-break galaxies and Lyα emitters, and therefore provide a local laboratory with which to study the extreme star formation processes that occur in high-redshift galaxies. Studying starbursting galaxies as a function of redshift is essential to understanding the build up of stellar mass in the Universe.Peer reviewe

The challenges of a public data release : behind the scenes of SDSS DR13

The Sloan Digitial Sky Surveys (SDSS) have been collecting imaging and spectoscopic data since 1998. These data as well as their derived data products are made publicly available through regular data releases, of which the 13th took place summer 2016. Although public data releases can be challenging to manage, they significantly increase the impact of a survey, both scientifically and educationally.Postprin

Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors

Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Limitations of Majority Agreement in Crowdsourced Image Interpretation

Crowdsourcing can efficiently complete tasks that are difficult to automate, but the quality of crowdsourced data is tricky to evaluate. Algorithms to grade volunteer work often assume that all tasks are similarly difficult, an assumption that is frequently false. We use a cropland identification game with over 2,600 participants and 165,000 unique tasks to investigate how best to evaluate the difficulty of crowdsourced tasks and to what extent this is possible based on volunteer responses alone. Inter-volunteer agreement exceeded 90% for about 80% of the images and was negatively correlated with volunteer-expressed uncertainty about image classification. A total of 343 relatively difficult images were independently classified as cropland, non-cropland or impossible by two experts. The experts disagreed weakly (one said impossible while the other rated as cropland or non-cropland) on 27% of the images, but disagreed strongly (cropland vs. non-cropland) on only 7%. Inter-volunteer disagreement increased significantly with inter-expert disagreement. While volunteers agreed with expert classifications for most images, over 20% would have been mis-categorized if only the volunteers’ majority vote was used. We end with a series of recommendations for managing the challenges posed by heterogeneous tasks in crowdsourcing campaigns

Galaxy Zoo:reproducing galaxy morphologies via machine learning

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural network is trained on a subset of objects classified by the human eye and we test whether the machine learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile-fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artifacts. Using a set of twelve parameters, the neural network is able to reproduce the human classifications to better than 90% for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine- learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes

Task workflow design and its impact on performance and volunteers' subjective preference in virtual citizen science

Virtual citizen science platforms allow non-scientists to take part in scientific research across a range of disciplines. What they ask of volunteers varies considerably in terms of task type, variety, user judgement required and user freedom, which has received little direct investigation. A study was performed with the Planet Four: Craters project to investigate the effect of task workflow design on both volunteer experience and the scientific results they produce. Participants' feedback through questionnaire responses indicated a preference for interfaces providing greater autonomy and variety, with free-text responses suggesting that autonomy was the more important. This did not translate into improved performance however, with the most autonomous interface not resulting in significantly better performance in data volume, agreement or accuracy compared to other less autonomous interfaces. The interface with the least number of task types, variety and autonomy resulted in the greatest data coverage. Agreement, both between participants and with the expert equivalent, was significantly improved when the interface most directly afforded tasks that captured the required underlying data (i.e. crater position or diameter). The implications for the designers of virtual citizen science platforms is that they have a balancing act to perform, weighing up the importance of user satisfaction, the data needs of the science case and the resources that can be committed both in terms of time and data reduction

Galaxy Zoo: Passive Red Spirals

We study the spectroscopic properties and environments of red spiral galaxies found by the Galaxy Zoo project. By carefully selecting face-on, disk dominated spirals we construct a sample of truly passive disks (not dust reddened, nor dominated by old stellar populations in a bulge). As such, our red spirals represent an interesting set of possible transition objects between normal blue spirals and red early types. We use SDSS data to investigate the physical processes which could have turned these objects red without disturbing their morphology. Red spirals prefer intermediate density regimes, however there are no obvious correlations between red spiral properties and environment - environment alone is not sufficient to determine if a spiral will become red. Red spirals are a small fraction of spirals at low masses, but are a significant fraction at large stellar masses - massive galaxies are red independent of morphology. We confirm that red spirals have older stellar popns and less recent star formation than the main spiral population. While the presence of spiral arms suggests that major star formation cannot have ceased long ago, we show that these are not recent post-starbursts, so star formation must have ceased gradually. Intriguingly, red spirals are ~4 times more likely than normal spirals to host optically identified Seyfert or LINER, with most of the difference coming from LINERs. We find a curiously large bar fraction in the red spirals suggesting that the cessation of star formation and bar instabilities are strongly correlated. We conclude by discussing the possible origins. We suggest they may represent the very oldest spiral galaxies which have already used up their reserves of gas - probably aided by strangulation, and perhaps bar instabilities moving material around in the disk.Comment: MNRAS in press, 20 pages, 15 figures (v3

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo