Consider what data science can do for highly complex fields like astronomy if it can help traditional industries like technology, manufacturing, and retail improve their operations. Countless amazing celestial objects are just waiting to be seen and discovered in infinite space. Now that they have the right technological tools and lightning-fast data science tools, astronomers can finally perfect their ability to make sense of extremely complex astronomical events near and far.
Data science developments in astronomy
DDA does exactly what it says on the tin; it generates astronomical knowledge from archived data sets that may or may not be directly related to the research at hand. Over seven years, astronomers were tasked with classifying 900,000 images obtained from the Sloan Digital Sky Survey to determine whether galaxies were elliptical or spiral and whether they were spinning or not. This task was completed in 2007 as part of the Galaxy Zoo project and is an excellent example.
Human analysis was nearly impossible due to the massive amount of data involved. One person must work nonstop for three to five years to complete it. Large empirical and simulation data sets require the development of new data science models. Solar missions, exoplanet surveys, sky surveys at various wavelengths, gravitational wave detectors, and large-scale astronomical simulations are all represented in these data sets. They work together to help astronomers achieve significant research objectives. Check out the trending data science course in Mumbai to gain in-depth knowledge of its tools and techniques.
Using Data Science in Astronomy to Improve Our Understanding of the Sun
The sun is arguably the planet's most powerful potential energy source. Solar energy is an important part of efforts to promote sustainability and clean energy, not only for solar power but also as a natural example of fusion energy. However, the number of information scientists can gather limits how much we can understand. For example, the horizontal motion of solar plasma is much more difficult to observe than the sun's temperature, but it holds the key to many of the sun's mysteries.
To address the issue, scientists from the United States and Japan built a neural network model that analyzed data from numerous simulations of plasma turbulence. After training the neural network with only temperature and vertical motion as references, it was possible to infer horizontal motion. This method has numerous applications in solar astronomy and studying fusion, fluid dynamics, and plasma physics. The new SUNRISE-3 balloon telescope will be used for high-resolution solar observations as part of other projects using this type of data.
Data Science for Astronomy through Crowdsourcing
Another common application of data science in astronomy is crowdsourcing, or the method of combining the efforts of thousands of "citizen scientists" to map the skies and analyze data at scale. The Exoplanet Explorers project discovered at least five exoplanets using data from the NASA Kepler space telescope. The fundamental discovery of this multi-planet system was based on crowdsourced data analysis efforts. Initially, research suggested a four-planet system, but later data analysis revealed the presence of a fifth planet. Over 14,000 volunteers participated in the crowdsourcing project, and more are still viewing and analyzing data as it comes in over time.
Astronomical Data Science: Investigating Mars
Researchers have been searching for signs of life on Mars for many years, and new robotic missions will soon return samples from the planet's surface. These missions will look for evidence of previous life in Mars sand samples by using mass spectrometry. Because of the massive amount of data to be analyzed, NASA will require new techniques to analyze samples quickly.
Scientists want to use machine learning techniques to crunch enormous data sets into new analytical models to automate the process of chemical analysis and draw significant conclusions more quickly. Individual competitors will need to develop machine learning models to help analyze and interpret data collected by missions in in-situ samples and lab instruments. Furthermore, the findings are expected to improve the speed and effectiveness of future Mars missions.
Are you looking for resources to learn data science and ML tools? Check out the machine learning course in Mumbai, and master them.