https://mundo.sputniknews.com/20211123/una-red-neuronal-descubre-cientos-de-nuevos-exoplanetas-1118535407.html

A neural network discovers hundreds of new exoplanets

A neural network discovers hundreds of new exoplanets

The new deep learning method added 301 new exoplanets to the long list of more than 4,500 that were already known. With this technological milestone, the … 23.11.2021, Sputnik Mundo

2021-11-23T04:32+0000

2021-11-23T04:32+0000

2021-11-23T04:35+0000

technology

🪐 astronomy

nasa

exoplanets

astrophysical journal

pleiades (supercomputadora)

kepler space telescope

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Deep learning is the name for the method used by neural networks to learn automatically based on a series of initial data. ExoMiner is a new neural network that takes advantage of NASA’s supercomputer, Pleiades. The network complements professionals in analyzing data and deciphering what is and what is not a planet, especially among the data collected by the Kepler space telescope, the humans working on the Kepler mission, with billions of stars in their field. vision and billions of potential exoplanets, have a daunting task as it takes a long time to study them all in detail. This is where ExoMiner comes in. When a planet passes between us and its star, we see the star dim slightly because the planet blocks some of its light. This is a method that scientists use to find exoplanets. Using different observation techniques, the network analyzes one by one the characteristics of each candidate to be an exoplanet. Using existing databases, ExoMiner validates likely candidates, comparing and marking how likely or unlikely a planet is. In their article for the Astrophysical Journal, the Ames Research Center (ARC) team in English) explains that the 301 exoplanets were found and marked as candidates by Kepler’s Office of Science, but only with the help of ExoMiner could enough data be processed to ensure that they were correct. of the recently confirmed planets is Earth-like or in the habitable zone of their stars. But they share similar characteristics to the general population of confirmed exoplanets in our galactic neighborhood. “These 301 discoveries help us better understand the planets and solar systems beyond our own, and what makes ours so unique,” adds Jon. Jenkins, ARC exoplanet specialist As the search for more exoplanets continues, scientists are looking to expand the positive experience shown by ExoMiner to other missions. In this way, they hope to speed up the count of the billions of distant planets that our galaxy is estimated to contain.

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https://mundo.sputniknews.com/20211026/detectan-senales-de-lo-que-podria-ser-el-primer-exoplaneta-fuera-de-la-via-lactea-1117512318.html

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2021

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Sputnik Mundo

🪐 astronomy, nasa, exoplanets, astrophysical journal, pleiades (supercomputer), kepler space telescope

The new deep learning method added 301 new exoplanets to the long list of more than 4,500 that were already known. With this technological milestone, scientists hope to speed up the count of the trillions of distant planets estimated to contain our galaxy.

Deep learning is the name for the method used by neural networks to learn automatically based on a series of initial data. ExoMiner is a new neural network that takes advantage of NASA’s supercomputer, Pleiades. The network complements professionals in analyzing data and deciphering what is and what is not a planet, especially among the data collected by the Kepler space telescope.

Humans working on the Kepler mission, with billions of stars in their field of view and trillion potential exoplanets, they have a titanic task since it takes a long time to study them all in a thorough way. This is where ExoMiner comes in.

When a planet passes between us and its star, we see that the star dims slightly because the planet blocks some of its light. This is a method that scientists use to find exoplanets. Using different observation techniques, the network analyzes one by one the characteristics of each candidate to be an exoplanet. Using existing databases, ExoMiner validates likely candidates, comparing and marking how likely or unlikely a planet is.

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In his article for the Astrophysical Journal, the Ames Research Center (ARC) team explains that the 301 exoplanets were found and marked as candidates by Kepler’s Office of Science, but only with the help of ExoMiner could the amount of data be processed enough to ensure they were right.

“When ExoMiner says that something is a planet, you can be sure that it is a planet. It is highly accurate and in a way more reliable than existing machine classifiers and human experts that it must emulate due to the biases that accompany it. human tagging, “explains Ames project leader and machine learning manager Hamed Valizadegan.

It is believed that none of the recently confirmed planets are similar to Earth or it is in the habitable zone of its stars. But they share similar characteristics to the general population of confirmed exoplanets in our galactic neighborhood.

“These 301 discoveries help us better understand the planets and solar systems beyond our own, and what makes ours so unique,” adds Jon Jenkins, exoplanet specialist at ARC.

As the search for more exoplanets continues, scientists are looking to expand the positive experience displayed by ExoMiner to other missions. In this way they hope speed up the count of the billions of distant planets estimated to contain our galaxy.

They detect signs of what could be the first exoplanet outside the Milky Way

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