How Does Data Science Help Astronomy?

By Author: vinod
Total Articles: 7
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Imagine what data science can accomplish for extremely complex subjects like astrophysics if it can help corporations in typical industries like technology, manufacturing, and retail better their operations. There are countless beautiful celestial objects out there waiting to be seen and discovered in boundless space. Now that they have the necessary technological tools and blazing-fast data science tools (many powered by AI and machine learning), astronomers can finally perfect their capacity to make sense of enormously complicated astronomical occurrences nearby and far away.

Advances in Data Science in Astronomy

Data-driven Astronomy (DDA) is the process of creating astronomical knowledge from archive data collections, which may or may not be directly connected to the subject at hand. A great example is the Galaxy Zoo project from 2007. Astrophysicists were required to categorize over 900,000 images from the Sloan Digital Sky Survey over 7years to identify whether galaxies were elliptical or spiral and whether they were rotating or not.

Human analysis was nearly impossible due to the enormous volume of ...
... data involved. One person would need to work nonstop for 3-5 years to complete it. New data science models must be developed to measure big empirical and simulated data sets. Data from solar missions, planetary surveys, sky surveys at various wavelengths, gravitational wave detectors, and large-scale astronomical simulations are all included in these data sets. They assist astronomers in achieving their significant scientific goals by working together.

Understandanting Our Sun

The sun is indeed the planet's greatest potential energy source. Not only for solar power but also as a natural example of fusion energy, solar energy is a crucial part of efforts to promote sustainability and clean energy. However, the information that scientists are able to gather limits how much we can grasp. For instance, although being far more difficult to observe than the sun's temperature, solar plasma's horizontal mobility is key to unlocking many of the sun's mysteries.

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To solve the problem, US and Japanese researchers build a neural network model to look at data from several plasma turbulence simulations. Using only the references of vertical motion and temperature after the neural network had been trained, it was possible to deduce horizontal motion. This technique has broad implications for solar astronomy and studies in fusion, fluid dynamics, and plasma physics. Additional investigations utilizing this type of data will also use high-resolution sun observations with the brand-new SUNRISE-3 balloon telescope.
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Crowdsourcing Astronomical Data Science

Crowdsourcing, or the method of utilizing thousands of "citizen scientists" to join their efforts to map the heavens and analyze data at scale, is another frequent application of data science in astronomy. At least five exoplanets were found by the Exoplanet Explorers project using information from the NASA Kepler satellite observatory (outside our own solar system). The whole discovery of this multi-planet system came from crowdsourced data analysis efforts. The fifth planet was discovered after more data processing, which contradicted earlier research that had shown a four-planet system. Nearly 14,000 people participated in the crowdsourcing project, and more continue to view and analyze data as it is gathered over time.

Data Science in Astronomy: Exploring Mars

Long-running robotic missions will soon bring samples from the surface of Mars to researchers looking for signs of life. The missions' primary method of examining samples of Martian sands for evidence of earlier life will be mass spectrometry analysis. Since there will be a tremendous amount of data to evaluate, NASA requires innovative techniques for analyzing the samples swiftly. NASA has created the Mars Spectrometry: Detect Evidence for Past Life challenge (with a prize of $30,000 for the most creative technique of analysis) in an effort to address the problem. NASA has teamed up with global crowdsourcing startup HeroX and data science provider DrivenData.

The scientists want to automate chemical analysis and fast-reach significant results by applying machine learning techniques, which include processing enormous data sets into new analytical models. Each participant will be required to create their own machine-learning models to enable analysis and aid in interpreting the data gathered by the missions using in-situ samples and lab equipment. Results are also anticipated to aid in the acceleration and efficiency of future Mars missions.

Conclusion

Data science is playing an increasingly important role in astronomy. Astronomers today confront the difficulty of sorting through and making sense of all this data due to the growth of telescopes and other technologies that can gather massive volumes of data. Data science is a critical component of this process and assists astronomers in developing new telescopes and devices and extracting knowledge and insights from massive data sets.

When applied to cutting-edge disciplines like astronomy, data science has great potential.
Our data-driven world is without a doubt getting more and more exciting all the time, whether it's thanks to the development of new ML models that can analyze data at fast speed, the collection of data from thousands of amateur astronomers, or the use of cutting-edge data science techniques by organizations such as NASA. Sign up for Learnbay's Data Science course in Dubai to build a successful data science career.