Machine learning yields a breakthrough in the study of stellar nurseries

Emission of carbon monoxide in the Orion B molecular cloud
Credit: J. Pety/ORION-B Collaboration/IRAM

Artificial intelligence can make it possible to see astrophysical phenomena that were previously beyond reach. This has now been demonstrated by scientists from the CNRS, IRAM, Observatoire de Paris-PSL, Ecole Centrale Marseille and Ecole Centrale Lille, working together in the ORION-B program. In a series of three papers published in Astronomy & Astrophysicson 19 November 2020, they present the most comprehensive observations yet carried out of one of the star-forming regions closest to the Earth.

The  in which stars are born and evolve are vast regions that are extremely rich in matter, and hence in . All these processes are intertwined at 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  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  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.

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