Purpose

Citizen science - the involvement of hundreds of thousands of people in the research process - is a radical response to the challenge of dealing with the greatly increased size of modern datasets. The combined assessment of many non-expert human classifiers with minimal training often equal or improves that of a trained expert and in many cases outperform the best automated algorithms. As astronomical surveys and observations have continued to grow towards the petabyte scale, online citizen science projects have proven quite successful in enlisting the general public to mine these rich datasets from searching for exoplanets to identifying gravitational lenses. The aim of this workshop is to bring together astronomers, computer scientists, and machine learning experts to focus on developing, for a citizen science approach to current and future astronomical datasets, new and improved data reduction techniques and algorithms to assess volunteer behavior and direct effort more efficiently. Such efforts are critical in coping with data from the next generation of experiments and instruments, including the Large Synoptic Survey Telescope (LSST), Hyper-Suprime Cam (HSC), and the Square Kilometer Array (SKA).

Organization

Day 1: The first day will be a series of invited talks designed to give an overview of the current state of citizen science in astronomy, highlight several astronomy-based online citizen science projects, introduce relevant analysis techniques employed by astronomers on these projects, and present the machine learning algorithms being developed for citizen science applications.

Days 2-4: Participants will divided into working groups to enable collaboration between astronomers, computer scientists, and machine learning experts during the remainder of the week. The working groups will focus on addressing topics including: bench marking on standard datasets the accuracy and efficiency of the current analysis techniques being employed, building new reduction techniques for combining volunteer classifications, and developing techniques to assess volunteer behavior.

Day 5: On the final day program participants will present their results, as well as plans for continued collaboration. There will also be time set aside for discussion of future citizen science opportunities and collaboration with ASIAA.

Confirmed Invited Speakers

• Michael Aye (UCLA)
• Steven Bamford (University of Nottingham)
• David Harvey (University of Edinburgh)
• Cliff Johnson (University of Washington)
• Chris Lintott (University of Oxford)
• Stuart Lynn (Adler Planetarium/Zooniverse)
• Phil Marshall (Kavli Institute for Particle Astrophysics and Cosmology/Stanford University)
• Karen Masters (University of Portsmouth)
• Meg Schwamb (ASIAA)
• Edwin Simpson (University of Oxford)
• Shiang-Yu Wang (ASIAA)

SOC

• Chris Lintott (University of Oxford)
• Stuart Lynn (Adler Planetarium/Zooniverse)
• Meg Schwamb (ASIAA)
• Rob Simpson (University of Oxford)
• Arfon Smith (GitHub Inc/Zooniverse)

LOC

• Meg Schwamb (ASIAA)
• Shiang-Yu Wang (ASIAA)
• Contact: wchenastro.ncu.edu.tw