Instituto de Astrofísica e Ciências do Espaço (IA) researchers Pedro Cunha and Andrew Humphrey tried to solve this classic problem by creating SHEEP, a machine learning algorithm that determines the nature of astronomical sources.

Figure 1: ML used in Astronomical objects.

Figure 1 shows SHEEP is a controlled machine learning pipeline that estimates photometric redshifts and uses this information to further classify sources as galaxies, quasars, or stars. “Photometric information is the easiest to obtain and therefore very important for the first analysis of the nature of observed sources,” says Pedro Cunha.

The team found that including redshift and object coordinates allows the AI to understand them in a 3D map of the universe, and they used this along with color information to better evaluate source properties.

Humphrey added: “When we allowed the AI to have a 3D view of the universe, it really improved its ability to make accurate decisions about what each celestial object is.” [1]

Vast-area surveys, each ground- and space-based, just like the Sloan Digital Sky Survey (SDSS), have yielded excessive volumes of information, revolutionizing the sector of astronomy.

Analyzing all the information utilizing conventional strategies will be time consuming. AI or machine studying will probably be essential for analyzing and making the perfect scientific use of this new information.

Pedro Cunha says, “One of the crucial thrilling elements is seeing how machine studying helps us to higher perceive the universe. Our methodology reveals us one doable path, whereas new ones are created alongside the method. It’s a thrilling time for astronomy.” [2]

Imaging and spectroscopic surveys are one of the main resources for the understanding of the visible content of the universe. The data from these surveys enables statistical studies of stars, quasars and galaxies, and the discovery of more peculiar objects.

Principal investigator Polychronis Papaderos says, “The development of advanced Machine Learning algorithms, such as SHEEP, is an integral component of IA’s coherent strategy toward scientific exploitation of unprecedentedly large sets of photometric data for billions of galaxies with ESA’s Euclid space mission, scheduled for launch in 2023.”

Euclid will provide a detailed cartography of the universe and shed light into the nature of the enigmatic dark matter and dark energy. [3]

References:

1. https://timetotimes.com/artificial-intelligence-helps-in-the-identification-of-astronomical-objects/
2. https://techyinsight.com/synthetic-intelligence-helps-within-the-identification-of-astronomical-objects/
3. https://technews.upjobsnews.com/artificial-intelligence-helps-in-the-identification-of-astronomical-objects-more-tech-news/

Cite this article:

Sri Vasagi K (2022), Astronomical Objects Can Acknowledge Using Machine Learning, Anatechmaz, pp. 336