Astronomy is all about data. The universe is getting bigger and so too is the amount of information we have about it. But some of the biggest challenges of the next generation of astronomy lie in just how we’re going to study all the data we’re collecting.
Copyright by theconversation.com
To take on these challenges, astronomers are turning to machine learning and artificial intelligence (AI) to build new tools to rapidly search for the next big breakthroughs. Here are four ways AI is helping astronomers.
1. Planet hunting
There are a few ways to find a planet, but the most successful has been by studying transits. When an exoplanet passes in front of its parent star, it blocks some of the light we can see.
By observing many orbits of an exoplanet, astronomers build a picture of the dips in the light, which they can use to identify the planet’s properties – such as its mass, size and distance from its star. Nasa’s Kepler space telescope employed this technique to great success by watching thousands of stars at once, keeping an eye out for the telltale dips caused by planets.
Humans are pretty good at seeing these dips, but it’s a skill that takes time to develop. With more missions devoted to finding new exoplanets, such as Nasa’s (Transiting Exoplanet Survey Satellite), humans just can’t keep up. This is where AI comes in.
Time-series analysis techniques – which analyse data as a sequential sequence with time – have been combined with a type of AI to successfully identify the signals of exoplanets with up to 96% accuracy.
2. Gravitational waves
Time-series models aren’t just great for finding exoplanets, they are also perfect for finding the signals of the most catastrophic events in the universe – mergers between black holes and neutron stars.
When these incredibly dense bodies fall inwards, they send out ripples in space-time that can be detected by measuring faint signals here on Earth. Gravitational wave detector collaborations Ligo and Virgo have identified the signals of dozens of these events, all with the help of machine learning. […]
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Read more: theconversation.com
Astronomy is all about data. The universe is getting bigger and so too is the amount of information we have about it. But some of the biggest challenges of the next generation of astronomy lie in just how we’re going to study all the data we’re collecting.
Copyright by theconversation.com
1. Planet hunting
There are a few ways to find a planet, but the most successful has been by studying transits. When an exoplanet passes in front of its parent star, it blocks some of the light we can see.
By observing many orbits of an exoplanet, astronomers build a picture of the dips in the light, which they can use to identify the planet’s properties – such as its mass, size and distance from its star. Nasa’s Kepler space telescope employed this technique to great success by watching thousands of stars at once, keeping an eye out for the telltale dips caused by planets.
Humans are pretty good at seeing these dips, but it’s a skill that takes time to develop. With more missions devoted to finding new exoplanets, such as Nasa’s (Transiting Exoplanet Survey Satellite), humans just can’t keep up. This is where AI comes in.
Time-series analysis techniques – which analyse data as a sequential sequence with time – have been combined with a type of AI to successfully identify the signals of exoplanets with up to 96% accuracy.
2. Gravitational waves
Time-series models aren’t just great for finding exoplanets, they are also perfect for finding the signals of the most catastrophic events in the universe – mergers between black holes and neutron stars.
When these incredibly dense bodies fall inwards, they send out ripples in space-time that can be detected by measuring faint signals here on Earth. Gravitational wave detector collaborations Ligo and Virgo have identified the signals of dozens of these events, all with the help of machine learning. […]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: theconversation.com
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