Artificial intelligence helps to identify astronomical objects

The classification of celestial objects is a long-standing problem. With sources at almost unbelievable distances, it is sometimes difficult for researchers to distinguish between objects such as stars, galaxies, quasars, or supernovae. Researchers at the Instituto de Astrofísica e Ciências do Espaço (IA), Pedro Cunha and Andrew Humphrey, tried to solve a classic problem by creating SHEEP, a machine learning algorithm that determines the nature of astronomical sources. Andrew Humphrey (IA and University of Porto, Portugal) comments: “The problem of classifying celestial objects is very complex in terms of the number and complexity of the universe, and artificial intelligence is a very promising tool for such tasks.”

Artificial intelligence helps in the identification of astronomical objects

SHEEP is a controlled machine learning pipeline that estimates photometric redshifts and uses this information to classify sources as galaxies, quasars, or stars. Before performing a classification, SHEEP first evaluates the photometric red deviations, which are then placed in the dataset as an additional function to train the classification model.

The team found that the inclusion of redshift and object coordinates allowed artificial intelligence (AI) to identify them on a three-dimensional map of the universe, and they used this along with color information to better assess the properties of the source. For example, AI has learned that the ability to find stars closer to the Milky Way plane is higher than at galactic poles. Humphrey added: “When we allowed AI to get a three-dimensional view of the universe, it really improved its ability to make accurate decisions about what exactly is a celestial object.”

Extensive surveys, both terrestrial and space, such as the Sloan Digital Sky Survey (SDSS), have yielded large amounts of data, revolutionizing astronomy. Future research by the Vira K. Rubin Observatory, the Dark Energy Spectroscopy (DESI), the Euclid Space Mission (ESA) or the James Webb Space Telescope (NASA / ESA) will continue to provide more detailed information and visualization. However, the analysis of all data using traditional methods can take a long time. AI or machine learning will be crucial for the analysis and best scientific use of this new data.
Euclid (ESA)
Euclid Mission (ESA)

Pedro Cunha says: “One of the most exciting parts is seeing how machine learning helps us better understand the universe. Our methodology shows us a possible path, while new ones are created in the process. This is a great time for astronomy. ”

Imaging and spectroscopic research are one of the main resources for understanding the visible content of the universe. The data from these surveys allow us to perform statistical studies of stars, quasars and galaxies, as well as to discover more unusual objects.

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