A scan of  news and blog posts at Zooniverse citizen science projects:

The Sunspotter project was introduced quietly earlier in the year and the first round of classifications  has been declared a success and a new one is underway:

•  Become a Sunspotter and play Solar ‘Hot-or-Not’ – Zooniverse
• …And We’re Back!! Sunspotter Round 2 – Sunspotter Blog

The goal

 is to determine the complexity of sunspot groups. It is well known (to solar physicists) that more complicated looking sunspot groups produce more solar flares than simple looking ones. But so far, scientists have not found a good way to quantify sunspot group complexity. This is not a task easily accomplished by a computer. Humans, on the other hand, can easily point to the more complex in a pair of objects, ideas, images, and so on.

The task is to judge relative complexity of sunspot groups:

The Sunspotter blog has a several interesting posts about the sun, sun spots, solar weather and more

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The Radio Galaxy Zoo project has turned up a pair of galaxies that seem to be connected despite measured at vastly different distances and velocities: Remarkable Discoveries Underway – Citizen Scientists fire up Radio Galaxy Zoo – Galaxy Zoo.

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Participants in the Planet Hunters project will look for exoplanets in data from the new K2 phase of the Kepler space observatory: Welcome to the Era of K2- Planet Hunters

“This is a photograph of the Milky Way with the approximate locations
of the 9 proposed Kepler K2 campaign target fields. The line shows
the ecliptic (Earth’s orbit plane) along which the Kepler Space
Telescope can maintain precision pointing. That line intersects the
galactic plane in two locations.
Credit: ESO/S. Brunier/NASA Kepler Mission.”
Click for higher resolution version.

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The Milky Way Project finds “a wonderful synergy that can exist between citizen scientists, professional scientists, and machine learning”  :  New MWP paper outlines the powerful synergy between citizens scientists, professional scientists, and machine learning – The Milky Way Project

A new Milky Way Project paper was published to the arXiv last week. The paper presents Brut, an algorithm trained to identify bubbles in infrared images of the Galaxy.

Brut uses the catalogue of bubbles identified by more 35,000 citizen scientists from the original Milky Way Project. These bubbles are used as a training set to allow Brut to discover the characteristics of bubbles in images from the Spitzer Space Telescope. This training data gives Brut the ability to identify bubbles just as well as expert astronomers!

The paper then shows how Brut can be used to re-assess the bubbles in the Milky Way Project catalog itself, and it finds that more than 10% of the objects in this catalog are really non-bubble interlopers. Furthermore, Brut is able to discover bubbles missed by previous searches too, usually ones that were hard to see because they are near bright sources.

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At first it might seem that Brut removes the need for the Milky Way Project –  but the ruth is exactly the opposite. This new paper demonstrates a wonderful synergy that can exist between citizen scientists, professional scientists, and machine learning. The example outlined with the Milky Way Project is that citizens can identify patterns that machines cannot detect without training, machine learning algorithms can use citizen science projects as input training sets, creating amazing new opportunities to speed-up the pace of discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone.

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A lot more data arrives for participants in the Disk Detective project to look for stars with disks of matter around them where planets could be forming: Good News Everyone! 272,000 More Subjects – Disk Detective.