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"Thank you for your efforts! We've completed our project! To browse other active projects that still need your classifications, check out zooniverse.org/projects.
No specialized background, training, or expertise in astronomy is required to participate in Zooniverse projects. We've designed the platform to be user-friendly, enabling anyone to contribute to real academic research using their computer and at their convenience.
Your contributions help us improve a machine learning classifier, which assists in identifying various interesting astronomical targets within and beyond the Milky Way. For detailed instructions, please visit the Classify section and follow the instructions provided.
You can help identify a range of astronomical objects, including:
Asteroids are identified by the streaks or trails they create as they move through the telescope's field of view during consecutive exposures. These streaks show up as lines with different colors corresponding to the various filters used during the exposures.
Galaxies are distinguished from stars by their extended structures consisting of billions to trillions of stars, gas, dust, and other matter. They exhibit various shapes, such as spiral, elliptical, or irregular, allowing differentiation from point-like star objects.
H-alpha emitters appear green in the images due to the specific filters used that emphasize the wavelength range associated with H-alpha emission. These emissions are indicative of regions of ionized hydrogen gas and active star formation within galaxies.
In addition to identifying specific astronomical objects, it's essential to recognize and understand undesired features in images, including:
The Zooniverse is the world's largest and most popular platform for people-powered research. Volunteers, numbering more than a million globally, come together to assist professional researchers in various projects. The aim is to enable research that would not be feasible or practical otherwise.
The desktop application provides an interactive and immersive learning experience for those with computer access, offering both identification and selection stages. On the other hand, the mobile application is tailored for individuals who primarily use smartphones and tablets, focusing on the identification stage.