Using training data prepared by humans, allowed AI to successfully classify galaxy morphologies with an accuracy of 97.5%. Then applying the trained AI to the full data set, it identified spirals in about 80,000 galaxies.
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This June, 2020, NASA announced that intelligent computer systems will be installed on space probes to direct the search for life on distant planets and moons, starting with the 2022/23 ESA ExoMars mission , before moving beyond to moons such as Jupiter’s Europa, and of Saturn’s Enceladus and Titan.
“This is a visionary step in space exploration.” said NASA researcher Victoria Da Poian . “It means that over time we’ll have moved from the idea that humans are involved with nearly everything in space, to the idea that computers are equipped with intelligent systems, and they are trained to make some decisions and are able to transmit in priority the most interesting or time-critical information”.
“When first gathered, the data produced by the Mars Organic Molecule Analyzer (MOMA) toaster-sized life-searching instrument will not shout out ‘I’ve found life here’, but will give us probabilities which will need to be analyzed,” says Eric Lyness, software lead in the Planetary Environments Lab at NASA Goddard Space Flight Center. “We’ll still need humans to interpret the findings, but the first filter will be the AI system”.
If artificial intelligence can search for alien life, it should be able to distinguish “galaxies with spiral patterns” from “galaxies without spiral patterns,” said Ken-ichi Tadaki, at the National Astronomical Observatory of Japan (NAOJ), who came up with the idea that using training data prepared by humans, allowed AI to successfully classify galaxy morphologies with an accuracy of 97.5%. Then applying the trained AI to the full data set, it identified spirals in about 80,000 galaxies.
The NAOJ research group, applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification. The AI enabled the team to perform the processing without human intervention.
“To find the very faint, rare galaxies, deep, wide-field data taken with the Subaru Telescope was indispensable,” said Dr. Takashi Kojima, about big data captured this June and the power of machine learning that led to the discovery of a galaxy with an extremely low oxygen abundance of 1.6% solar abundance, breaking the previous record of the lowest oxygen abundance. The measured oxygen abundance suggests that most of the stars in this galaxy were formed very recently.
Automated processing techniques for extraction and judgment of features with deep-learning algorithms have been rapidly developed since 2012. Now they usually surpass humans in terms of accuracy and are used for autonomous vehicles, security cameras, and many other applications.
Now that this technique has been proven effective, it can be extended to classify galaxies into more detailed classes, by training the AI on the basis of a substantial number of galaxies classified by humans.
NAOJ is running a citizen-science project “GALAXY CRUISE,” where citizens examine galaxy images taken with the Subaru Telescope to search for features suggesting that the galaxy is colliding or merging with another galaxy. The advisor of “GALAXY CRUISE,” […]
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