We live in a society where science is little more than a “spectator sport” for most of us who have an interest in it. Data collection and original research often require substantial investments of time and money, as well as a long-term commitment. Those of us who are already working full time and, in spite of that, have little discretionary income, often find “participatory science” out of reach, no matter how great our enthusiasm or aptitude.
As today’s scientific instruments increasingly generate enormous quantities of data, the people who “do science” for a living are too few in number to analyze all that data. Fortunately, this is one area where “citizen scientists” can help.
There are a number of interesting scientific projects that lend themselves well to “crowd sourcing”, and Zooniverse is a portal to many of them.
Here are the currently active Zooniverse projects in the disciplines of astronomy and physics.
Backyard Worlds: Planet 9
Discover new brown dwarfs and possibly a new solar system planet by scrutinizing images from the Wide-field Infrared Survey Telescope (WISE).
Discover new comets previously misidentified as asteroids by analyzing deep images taken by the Subaru 8.2-meter telescope in Hawaii.
Help search for stars with undiscovered disks of dust around them. These stars show us where to look for planetary systems and how they form.
Discover transiting exoplanet candidates in Kepler’s K2 data.
Galaxy Zoo Galaxy Zoo: 3D
Classify galaxies, many of which have never been studied before, and look for unusual features.
Identify and characterize “glitches” in LIGO data to make it easier to identify gravitational wave events.
Help search for unknown exotic particles in data from the Large Hadron Collider (LHC), the world’s largest and most powerful particle collider.
Milky Way Project
Classify images from two infrared space telescopes: the Spitzer Space Telescope (SST) and the Wide-field Infrared Survey Telescope (WISE).
Identify and measure features on the surface of Mars.
Discover transiting exoplanet candidates in data from the Kepler spacecraft.
Radio Galaxy Zoo
Search radio images of galaxies for evidence of jets caused by matter falling into supermassive black holes.
Radio Meteor Zoo
Identify meteors through the reflection of radio waves from their ionization trails.
Solar Stormwatch II
Characterize solar storms and their interaction with the solar wind through the analysis of images from NASA’s twin Solar Terrestrial Relations Observatory (STEREO) spacecraft.
Scrutinize the most recent images collected by the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) in Hawaii in comparison to reference images to discover new supernovae that can then be immediately followed by ground-based and space-based telescopes.
All of these projects utilize “machine learning” computer algorithms such as neural networks and random forests (artificial intelligence, or AI) to some extent, and in fact citizen scientist participants help “train” these algorithms so they do a better job of finding or classifying or whatever. For a great introduction to this subject, see “Machines Learning Astronomy” by Sky & Telescope news editor Monica Young in the December 2017 issue, pp. 20-27.
As machine learning algorithms get better and better, they may no longer need citizen scientists to train them.
In the meantime, have fun and contribute to science!