The new AI deep learning tools that allows astronomers to explore deep space

Most galaxies in the universe live in low density environments known as terrain or in small groups like this that rarely occur in our galaxy of the Milky Way and Andromeda.

but these galaxies are the most extreme environments in which galaxies live and learn. Understanding dark matter and dark energy is better

Galaxy clusters:

In the pioneering 50s of the last century in the discovery of galaxy clusters, astronomer George Abell, he spent years searching for galaxy clusters using eyes, magnifying glass, photo plates to recognize them. Manually analyze around 2,000 photographic plates and look for visual clusters of galaxy

clusters and detailed astronomical coordinates of dense galaxy regions. His work leads to the “Abel Catalog” of galaxy clusters in the northern hemisphere.

Deep CEE is in an approach to identifying Abell galaxy clusters, but replaces astronomers with AI models, who are trained to “observe” color images and identify galaxy clusters. This is a modern model based on neural networks that studies the way the human brain is designed to mimic objects by activating certain neurons when different patterns and colors appear to be detected.

Astronomical objects:

Chan trained the AI by repeatedly displaying examples of objects that were known and labeled in the image, while the algorithm could not learn to assign only objects. A pilot study was then conducted to test the ability of algorithms to identify and classify galaxy clusters in images containing many other astronomical objects.

The CEE Deep Study of Deep Sky Digital Sloan has been successfully implemented Chan said, Finally, we will use our revolutionary study model as a large synoptic telescope LSST, which will examine a wider and deeper region in the universe that has never been examined.

Human interaction:

New modern telescopes allow astronomers to monitor a wider and deeper range than ever before by studying the large-scale structure of the universe and mapping it to vast undiscovered content.

By automating the recognition process, scientists can quickly scan sets of images and restore accurate predictions with minimal human interaction. This will be very important in the future for data analysis. The upcoming LSST Sky Survey (expected in 2021) will map the sky in the southern hemisphere and produce around 15 TB of data every night.

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