In this magical world of technology, if there is one term that has taken everyone by storm, then it has to be – Artificial Intelligence, without a doubt. Over the years, Artificial Intelligence, or AI as it is commonly called, has seen varied applications in every field possible – be it healthcare, e-commerce, robotics and even banking for that matter. However, that one field that is relatively less explored but is as interesting and exciting as the rest is astronomy.

Talking about astronomy, one of the toughest challenges lies in studying the data. It is because of this that astronomers are turning towards machine learning and Artificial Intelligence (AI) in order to build new tools. Having said that, have a look at how has Artificial Intelligence transformed astronomy and is catering to the needs of astronomers.

• Off late, a group of astronomers made use of Artificial Intelligence in a galaxy merger study to confirm that the driving force behind starbursts was nothing but galaxy mergers. Considering how large the database turned out to be, the team had built a deep learning algorithm that taught itself to identify merging galaxies. One of the astronomers was quoted as saying that the advantage of Artificial Intelligence is that it would improve the reproducibility of the study. The reason being – the algorithm is consistent in its definitions of a merger.

• One of the most crucial tasks to accomplish for an astronomer is to hunt a p The whole science behind this is – Whenever there is an exoplanet passing in front of its parent star, some of the light is blocked, which the humans can see. It is here that the astronomers observe the orbits of an exoplanet and build a picture of the dips in the light. After doing so, they identify various properties of the planet such as its mass, size and distance from its star to name a few. That said, AI turns out to be no less than a saviour here. By virtue of AI’s time-series analysis techniques, it is possible to analyse data as a sequential sequence and thus identify the signals of exoplanets with as much as up to 96 percent accuracy.

• The changing sky – The changing sky has grabbed all the attention for it has made its way to become one of the most remarkable projects of all time. This aims to survey the entire night sky every night – collecting over 80 terabytes of images in one go to be able to learn how the stars and galaxies in the universe vary with time.

• Gravitational waves – Finding the signals of the most catastrophic events in the universe is very important for astronomers. What happens here is – when exoplanets fall inwards, they send out ripples in space-time. These can further be detected by measuring faint signals here on Earth. Gravitational-wave detector collaborations – Ligo and Virgo have worked exceptionally well in this aspect. Both of them have been successful in identifying signals with the help of machine learning. The astronomers now get alerts as a result of which they can turn their telescopes in the right direction.

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Data science is diligently being used in the field of astronomy and here’s how-

Use of data science in astronomy can be largely found in the concept of data-driven astronomy (DDA). It is the production of astronomical knowledge based on archival data sets. DDA is similar to industrial data science in a way that the data sets are not taken with the experiment in mind, but rather are a byproduct of other processes or investigations. Successful DDA needs to be quite close to the data and often involves working closely with instrument and survey specialists.

To further understand the impact of data science on astronomy, one must go through the famous Galaxy Zoo project from 2007. Kevin Schawinski, an astrophysicist at Oxford University, had the task to classify 900,000 images of galaxies gathered by the Sloan Digital Sky Survey for a period of 7 years. He had to look at every single image and note whether the galaxy was elliptical or spiral and if it was spinning. The task seems like a pretty trivial one. However, the huge amount of data made it almost impossible. Because, according to the estimations, one person had to work 24/7 for 3-5 years in order to complete it. So, Schawinski and his colleague Chris Lintott came up with a better idea. This is where they decided to apply the technique of data science to analyze those images.

Currently, researchers are involved with specific projects including large empirical and simulation data sets: spacecraft imaging data from solar system missions, spacecraft survey data for exoplanets, sky surveys at radio, infrared, and optical wavelengths, data sets from gravitational wave detectors, and cosmological simulations of large-scale structures in the universe. These data sets will provide attractive contexts for learning and applying modern analysis methods.

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