- 1 hr ago Flipkart Big Billion Days Sale 2021: Realme, Poco, Samsung, Oppo, Motorola Planed To Launch New Smartphones
- 7 hrs ago Realme GT Neo2 Price Revealed Ahead Of Launch; New Dimensity 1200AI SoC Variant Also Tipped
- 8 hrs ago Paytm Mall Budget Days Sale: Discount Offers On Budget Smartphones
- 8 hrs ago JioPhone Offering 24GB Data With Most Expensive Plan: How To Get It
- Sports IPL 2021: Rajasthan Royals pacer Kartik Tyagi ecstatic after match-winning final over against Punjab Kings
- Movies Khatron Ke Khiladi 11 Winner Is Arjun Bijlani? Netizens Congratulate The Naagin Star Ahead Of KKK 11 Finale
- News Afghanistan crisis, China's assertiveness, ways to contain terrorism to figure in Modi-Biden talks
- Finance 2 Stocks To Buy By HDFC Securities For Potential Upside Up To 54%
- Automobiles Yamaha Aerox 155 Launched In India; Powered By R15 Sourced 155cc Engine With VVA
- Education SBI Clerk Prelims Result 2021 Declared, Check Direct Link
- Lifestyle Emma Corrin’s Emmy’s 2021 Costume Is Surprising And There Could Be Salem Witch Trials Angle To It
- Travel 8 Beautiful Long Drive Routes In India
How Is AI Helping Astronomers Unfurl Mysteries Of The Universe?
With advancements in astronomy, the amount of data about interstellar objects has also grown immensely. However, collecting this data is not the challenge, studying all of it is! To make their lives easy, scientists have turned to artificial intelligence (AI) and machine learning (ML) to develop tools that can help find the next breakthrough. Here's how AI is coming in the aid of scientists and space agencies.
Searching For Other Worlds
Studying transits has been deemed as the most efficient way to hunt for planets. It means looking for a change in the luminosity of a parent star when an exoplanet passes in front of them blocking some light. This helps scientists determine the size and mass of the planet and its distance from its star.
NASA's Kepler space telescope uses this method to observe thousands of stars at once. While humans can see these dips in the light of stars, there's a possibility of missing them as well. This is where AI steps in. The space agency's TESS uses time-series analysis techniques that combine data with AI algorithm to identify exoplanets with around 96% accuracy.
Different Faces Of The Sky
To observe the changes in galaxies in the universe with time, a lot of data needs to be collected. Vera Rubin Observatory in Chile is capable of capturing 80 TB of images of the night sky in one survey. In the coming years, the observatory will capture and process hundreds of petabytes of data. For the unversed, 100 petabytes is equivalent to every photo ever uploaded on Facebook.
This gargantuan amount of data is impossible for humans to process. That's when machine learning would be used to search these surveys and process only the important data. For instance, a machine-learning algorithm will be put in place to search images of rare interstellar events such as supernovae, while another algorithm could look for other planetary systems.
With the help of AI, astronomers can train computers to identify the signals of specific cosmic phenomena. This will allow them to provide very detailed data to the concerned people.
Hunting The Rarest Interstellar Objects
With more data being collected every day about the universe, there is a need to scrap the data that isn't useful. So can scientists find rare objects in the universe with such a huge amount of data? One cosmic phenomenon that intrigues many scientists is strong gravitational lenses which occur when two galaxies align in a way to magnify more distant objects.
But finding these lenses in the vast universe is very difficult. It's something that will only get harder as more data on galaxies continues to pour in. In the coming years, astronomers will collect huge amounts of data using new instruments. This will increase their reliance on machine learning and artificial intelligence to make breakthrough discoveries.
Diving Deep In Black Holes
The Time-series model not only hunts for exoplanets but can also find signals of space catastrophes such as black holes and neutron star mergers. Such events cause ripples across space-time and can be detected by measuring signals that reach our planet. Many such events have been observed by gravitational wave detectors using machine learning.
Training models that use simulated data of black hole mergers enable the wave detectors to detect potential events with moments and prompt astronomers across the globe to turn their telescopes in the direction of the event.