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Citizen Science (& Public Engagement)

Citizen Science (& Public Engagement). C. Christian HST Outreach Project Scientist. Citizen Science. What is it?. Voluntary participation of non-expert individuals i n scientific research. Citizen Science detail. Volunteers perform tasks that contribute to research

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Citizen Science (& Public Engagement)

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  1. Citizen Science (& Public Engagement) C. Christian HST Outreach Project Scientist

  2. CitizenScience What is it? Voluntary participationofnon-expert individuals in scientificresearch

  3. CitizenScience detail • Volunteers perform tasks that contribute to research • Research problems require large numbers of individuals to apply cognitive skills • Studies cannot be performed through algorithms • Outcomes • Refereed research papers • Machine learning • Creation of interested community • Potential for education applications

  4. 40,000 separate exposures of M31 Panchromatic Hubble Andromeda Treasury (PHAT) survey ~3000 star clusters 8 members science team one month searching the first ~20% of the survey’s imaging   ~600 likely star clusters 4x number previously known in same region

  5. Launched on December 5th 2012 • Examine ~12,000 image cutouts • ~7,000 unique visitors and more than 100,000 image classifications in the first day.   • Overall classification rate that is greater than one per second! •  After 16 days, concluded data collection after amassing over 1 million image classifications, = 80 classifications per image

  6. M83

  7. M83 Volunteer classification

  8. Integrate Citizen Science • User selected patches of sky • Examine transient alerts • Examine light curves • Classify light curves • Compile statistics of transient types • Identify unusual objects in field • Moving objects • Improve algorithms • Correspond with other users LSST

  9. Possible Citizen Science • Examine transients, light curves, • visual appearance • Classify light curves • known types, new types • Comet hunting • Other solar system objects • Extra-galactic objects – mergers, streams, debris • Improve algorithms • Correspond with other users PanSTARRs

  10. Outcomes, Publications • Galaxy Zoo (SDSS) - 33 publications • The Galaxy Zoo survey for giant AGN-ionized clouds: past and present black hole accretion events 2012 W Keel etal MNRAS 420 878 • Galaxy Zoo: reproducing galaxy morphologies via machine learning 2010 M Banerjietal MNRAS 408 342 • Kepler - 5 publications • Planet Hunters: A Transiting Circumbinary Planet in a Quadruple Star System 2012 M. Schwambetaleprint arXiv:1210.3612 • Planet Hunters: Assessing the Kepler Inventory of Short-period Planets 2012 M SchwambetalApJ754 129S • Moon Zoo – 11 publications • Moon Zoo: First Science Results 2010 C. Lintott European Planetary Science Congress • Milky Way Project • The Milky Way Project: Leveraging Citizen Science and Machine Learning to Detect Interstellar Bubbles, C. Beaumont, etal, 2014 ApJS • HST Cosmos • Galaxy Zoo: an independent look at the evolution of the bar fraction over the last eight billion years from HST-COSMOS, T. Melvin 2014, MNRAS  Challenge is to motivate science teams to consider Citizen Scientist tasks as a critical aspect of data processing pipelines. Worth the investment!

  11. “Citizen Science: Contributions To Astronomy Research”, Christian. C., Lintott, C., Smith, A. Bamford, S.,2012.

  12. CitSci Project Definition Phase • Define the research task • Define the data set • Envision the interface • Specify what volunteer input will be codified • Create a storyboard • Educational scaffolding? • Plan the technical/scientific support for development • Development timeline • Beta testing • Deployment • Analysis, publication, dissemination

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