Artificial intelligence is a broad term and has several applications. Astronomers have been using AI to study the celestial bodies, making their work faster and easier.
How AI Helps Astronomers?
Astronomy has a long history of searching for patterns among vast volumes of data, unintentional discoveries, and a close relationship between theory and observation. Artificial intelligence (AI) makes astronomy faster and more powerful than ever.
The neural networks most frequently used by astronomers allow the computer to learn about all the connections in a training data set and then apply that information to real data sets.
Inaccuracies, untidy foregrounds, pollutants, artifacts, and noise abound in raw astronomy photos. It takes a lot of work to process and clean these photos to make them presentable and valuable for scientific inquiry. This work is typically done partially by automated systems and partially manually.
Astronomers are increasingly using artificial intelligence to analyze the data and remove any unnecessary portions of the photos to produce a clear result. For instance, in April 2023, a machine learning "makeover" was applied to a 2019 image of the supermassive black hole at the center of the galaxy Messier 87 (M87), producing a significantly sharper image of the black hole's structure.
Another illustration is when astronomers feed galaxies' images into neural network algorithms and provide the algorithms with the classification rules for newly discovered galaxies. The classifications in place were created manually, either by the researchers themselves or through volunteer citizen science projects. With a training set, the neutral network can automatically identify the galaxies in real data, significantly quicker and less prone to error than manual classification.
Astronomers can also use AI to clean up photos of space captured by ground-based telescopes, including optical interference due to the Earth's atmosphere.
AI for Search of Extraterrestrial Life
An accurate test for the presence of extraterrestrial life has recently been developed, and it has a 90% accuracy rate, according to a team of scientists led by Carnegie's Robert Hazen and visiting scholar Jim Cleaves from the Tokyo Institute of Technology and the Blue Marble Space Institute for Science. Their artificial intelligence-based technique distinguished between samples of biological origin from the present and the past. The new test accurately determines if anything that was ever living has ever been a part of a sample's past.
Hazen claims that this widely used analytical method can revolutionize the search for extraterrestrial life and increase our understanding of the chemistry and origin of the first life on Earth. It enables intelligent sensors on robotic spacecraft, rovers, and landers to look for signs of life before the samples are transmitted to Earth.
The revolutionary analytical method depends on more than pinpointing a specific chemical or compositional group in a sample; instead, by identifying minute variations in the molecular patterns of a sample as revealed by pyrolysis, gas chromatography analysis-which separates and identifies a sample's parts, followed by mass spectrometry-which determines the molecular weights of those components. The study demonstrated how AI could differentiate between biotic and abiotic samples.
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