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A recent paper reports that 10,000 variable stars were discovered by citizen scientists who parsed data from telescopes all over the globe.
Since January, the volunteers have been studying data from All-Sky Automated Survey for Supernovae. The Ohio State University researchers manage the ASAS-SN survey.
The researchers posted a paper to the arXiv Preprint Server detailing what Citizen ASASSN has achieved so far. More than 3100 volunteers have completed about 839,000 classifications with more than 100,000 light curves. This data is used by astronomers to determine the location of objects in the sky. Variable stars are stars whose brightness changes with time. The light we see from such stars is not constant.
Volunteer scientists tried to classify the stars broadly as eclipsing binaryaries, which is where one star passes infront of another, pulsating and rotating stars. Data could also be classified as junk by volunteer scientists, meaning that it was not a star. Low-Earth orbiting satellites, for example, can block the light of stars in telescopes. Data from such a satellite would therefore be considered junk. Volunteer scientists can mark data as "unknown" when the light curves do not match any of the other classes.
Researchers at Ohio State were able to verify the accuracy of some of the stars that the volunteer scientists classified.
Collin Christy (lead author and ASAS-SN analyst) said, "It turns out that they were quite accurate." "Our users were very good at finding the eclipsing or pulsating systems within our data.
Researchers found that users could also identify junk data easily.
This project is a continuation of ASAS-SN’s ongoing and previous work to search the sky for black holes and other phenomena within the cosmos. ASAS-SN recently upgraded its telescopes, allowing astronomers access to deeper space for new supernovae, variable stars and other objects. Machine-learning algorithms are used to classify data. This is a method that has been used extensively in the analysis of ASASSN's data.
Tharindu Jayasinghe (a doctoral student and Ohio State presidential fellow) stated that the main purpose of the paper was to share data and science with a wider community. This has allowed us to engage with the public. People contribute to science and scientists can engage with the public through these platforms. Users can ask questions and we answer. We also teach people how to do science. It's a win/win situation.
Jayasinghe stated that citizen scientists are also helping to improve the machine learning algorithm. Their input helps the machine understand which data is useless and what is junk.
Jayasinghe stated that "the human eye can spot unusual things and point them out better than the machine, and when they do that, it allows us make these really amazing discoveries." If given the chance, humans can accomplish amazing things."
Continue reading Astronomers discover a chromospherically-active eclipsing binary system
C. T. Christy et. al., Citizen ASASSN Data Release I: Variable Star Classification Using Citizen Science. arXiv (2021). arXiv.2111.02415 [astro.SR] Journal information. arXiv C. T. Christy et al., Citizen ASASSN Data Release I. Variable Star Classification Using Citizen Science. (2021). arXiv:2111.02415 [astro-ph.SR] arxiv.org/abs/2111.02415