74 research outputs found
Galaxy Zoo:chiral correlation function of galaxy spins
No full textGalaxy Zoo is the first study of nearby galaxies that contains reliable information about the spiral sense of rotation of galaxy arms for a sizeable number of galaxies. We measure the correlation function of spin chirality (the sense in which galaxies appear to be spinning) of face-on spiral galaxies in angular, real and projected spaces. Our results indicate a hint of positive correlation at separations less than ~0.5 Mpc at a statistical significance of 2-3 sigma. This is the first experimental evidence for chiral correlation of spins. Within tidal torque theory it indicates that the inertia tensors of nearby galaxies are correlated. This is complementary to the studies of nearby spin axis correlations that probe the correlations of the tidal field. Theoretical interpretation is made difficult by the small distances at which the correlations are detected, implying that substructure might play a significant role, and our necessary selection of face-on spiral galaxies, rather than a general volume-limited sample
The Evolving Nature of Astronomy Research and its Implications for EPO
No full textLike all sciences in general, astronomy has experienced unprecedented changes in just the last few years. As the total volume of data continues to double each year, we struggle to collect and interpret all the data - and as EPO professionals, we struggle to bring this data to learners in a meaningful way. This rapporteur paper will review some of our community’s reactions to the “Evolving Nature of Astronomy Research” thread. These reactions include strategies for learners to access real astronomy data through visualization and resources like the World Wide Telescope, for remote telescope access, for collaborative environments, and for citizen science projects. These resources come together in a variety of activities for learners in all settings, at all levels. As these activities are deployed and evaluated, astronomy education will continue to benefit from its rich interconnections with astronomy research
Galaxy Zoo:reproducing galaxy morphologies via machine learning
No full textWe 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
First You Get the Money, Then You Get the Reviews, Then You Get the Internet Comments: A Quantitative Examination of the Relationship Between Critics, Viewers, and Box Office Success
No full textThis article examines the relationships between a movie's perceived artistic merit as ranked by critics, a movie's quality as ranked by viewers, a movie's gross box office take, and its release date
Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies
No full textWe 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
The Universe Online
No full textData from the Sloan Digital Sky Survey can be used by students, teachers, and the public to contribute to scientific research.</jats:p
Galaxy Zoo:building the low-mass end of the red sequence with local post-starburst galaxies
No full textWe present a study of local post-starburst galaxies (PSGs) using the photometric and spectroscopic observations from the Sloan Digital Sky Survey (SDSS) and the results from the Galaxy Zoo project. We find that the majority of our local PSG population have neither early- nor late- type morphologies but occupy a well-defined space within the colour-stellar mass diagram, most notably, the low-mass end of the "green valley" below the transition mass thought to be the mass division between low-mass star-forming galaxies and high-mass passively-evolving bulge-dominated galaxies. Our analysis suggests that it is likely that a local PSG will quickly transform into "red", low-mass early-type galaxies as the stellar morphologies of the "green" PSGs largely resemble that of the early-type galaxies within the same mass range. We propose that the current population of PSGs represents a population of galaxies which is rapidly transitioning between the star-forming and the passively-evolving phases. Subsequently, these PSGs will contribute towards the build-up of the low-mass end of the "red sequence" once the current population of young stars fade and stars are no longer being formed. These results are consistent with the idea of "downsizing" where the build-up of smaller galaxies occurs at later epochs
Galaxy Zoo: Motivations of Citizen Scientists
No full textCitizen science, in which volunteers work with professional scientists to conduct research, is expanding due to large online datasets. To plan projects, it is important to understand volunteers’ motivations for participating. This paper analyzes results from an online survey of nearly 11 000 volunteers in Galaxy Zoo, an astronomy citizen science project. Results show that volunteers’ primary motivation is a desire to contribute to scientific research. We encourage other citizen science projects to study the motivations of their volunteers, to see whether and how these results may be generalized to inform the field of citizen science
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