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Machine Learning Leads to a Breakthrough in Study of Stellar Nurseries
News

Machine Learning Leads to a Breakthrough in Study of Stellar Nurseries

Machine Learning Leads to a Breakthrough in Study of Stellar Nurseries
News

Machine Learning Leads to a Breakthrough in Study of Stellar Nurseries

Credit: J. Pety/ORION-B Collaboration/IRAM
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The gas clouds in which stars are born and evolve are vast regions of the Universe that are extremely rich in matter, and hence in physical processes. All these processes are intertwined on different size and time scales, making it almost impossible to fully understand such stellar nurseries. However, the scientists in the ORION-B* program have now shown that statistics and artificial intelligence can help to break down the barriers still standing in the way of astrophysicists.

With the aim of providing the most detailed analysis yet of the Orion molecular cloud, one of the star-forming regions nearest the Earth, the ORION-B team included in its ranks scientists specializing in massive data processing. This enabled them to develop novel methods based on statistical learning and machine learning to study observations of the cloud made at 240 000 frequencies of light**.

Based on artificial intelligence algorithms, these tools make it possible to retrieve new information from a large mass of data such as that used in the ORION-B project. This enabled the scientists to uncover a certain number of 'laws' governing the Orion molecular cloud.

For instance, they were able to discover the relationships between the light emitted by certain molecules and information that was previously inaccessible, namely, the quantity of hydrogen and of free electrons in the cloud, which they were able to estimate from their calculations without observing them directly. By analyzing all the data available to them, the research team was also able to determine ways of further improving their observations by eliminating a certain amount of unwanted information.

The ORION-B teams now wish to put this theoretical work to the test, by applying the estimates and recommendations obtained and verifying them under real conditions. Another major theoretical challenge will be to extract information about the speed of molecules, and hence visualize the motion of matter in order to see how it moves within the cloud.

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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