Growing Role of Data Science in Space Technology

Summary: Data Science drives advancements in space exploration, from analysing solar plasma to crowdsourcing exoplanet discoveries and improving Mars exploration with Machine Learning, highlighting its critical role in modern astronomy.

Introduction 

With technological advancement and large volumes of data accumulating in different organisations and businesses, Data Science has become crucial. Companies are realising the importance of Data Science in in Space technology. Can you imagine how it is advancing even in space or astronomy? 

Space has infinite celestial objects waiting to be discovered and seen by the world. With technological advancements, the future of astronomy seems brighter. With the exponential use of technological tools and fast-blazing Data Science tools powered by Artificial Intelligence (AI) and Machine Learning, astronomers can finally use their skills ideally.

Astronomers can now make perfect sense of astronomical events close to the Earth and those far away. While we all know how extremely complex these events might be, astronomers will have the capability to deal with them effectively. Thus, the future will find AI in space exploration as one of the most crucial prospects in astronomy. 

Thus, AI in space exploration will be one of the most crucial prospects in astronomy in the future. The following blog will focus on Data Science in Space Exploration, emphasising Data Science’s advancements in space technology, understanding the sun, astronomical Data Science using crowdsourcing, and exploring Mars. 

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Role of Data Science in Space Exploration 

Astronomy has become immensely Data-driven and does what it says. Data-driven astronomy can create astronomical knowledge based on archived datasets, which may or may not be directly relevant to the research that you have at hand. 

One of the most significant accomplishments in the history of astronomy has been the classification of 900,000 images obtained from the Sloan Digital Sky Survey. Over four years, astronomers determined whether galaxies were elliptical or spiral and whether they were spinning. 

This task was part of the Galaxy Zoo project in 2007. However, human-based analysis was nearly impossible because of the enormous amount of data involved. With the help of new Data Science models developed by experts, large empirical and simulation datasets will be possible. 

These datasets include data from solar missions, exoplanet surveys, sky surveys at various wavelengths, gravitational detectors of waves, and large-scale astronomical simulations. By working together, astronomers can achieve their significant research goals. 

Understanding the Sun through Data Science 

We all know that the sun is the most significant source of potential energy for our planet. It can be used for solar power and natural instances of fusion energy. Effectively, as a means of promoting sustainability and using clean energy, solar energy is the driving force for maintaining ecological balance. 

However, while scientists may have large volumes of information with them, their accessibility is minimal. For instance, if they have to understand the horizontal motion of solar plasma, it is much more difficult for them to observe than the sun’s temperature. Hence, many of the sun’s mysteries remain unresolved. 

To address the issue, scientists from all over the world, including the US and Japan, constructed a neural network model for analysing data from various simulations of plasma turbulence. After training the neural network, it was possible to infer the horizontal motion using only the temperature and vertical motion to reference the event. 

The method has comprehensive applications for solar astronomy. Its impact in other sectors, such as plasma physics, fusion research studies, and fluid dynamics, is also practical. The same method will also be used to conduct high-resolution solar observations with the help of the new SUNRISE-3 balloon telescope. 

Using Astronomical Data Science through Crowdsourcing

The role of Data Science in the Space Industry is highly effective, even in the case of crowdsourcing. Crowdsourcing uses thousands of “citizen scientists” to combine their efforts to map the skies and analyse the data on a massive scale. 

Data Science in Space technology has enabled even NASA experts to find at least five exoplanets. The Exoplanet Explorers project found these exoplanets using information from the NASA Kepler space telescope.

The discovery was considered to be a multi-planet system that came from crowdsourcing data analysis efforts. Initial research conducted by the experts indicated that there were four planets. At the same time, later data analysis revealed a fifth planet. 

With the help of this research, the crowdsourcing project brought around 1400 volunteers. 

Further, more students and researchers are conducting data analyses to view and analyse as developments occur over time. 

Exploring Mars through Data Science in Astronomy 

Future robotic missions will collect samples from the planet’s surface shortly as part of the long-running hunt for evidence of life on Mars. These missions will examine Martian sand samples using mass spectrometry to look for any signs of earlier life. NASA is looking for novel methods for quick analysis due to the enormous volume of data that needs analysis. 

NASA developed the “Mars Spectrometry: Detect Evidence for Past Life challenge,” which offers a $30,000 prize for the most creative analytical approach, in partnership with HeroX, the world’s largest crowdsourcing company, and Data Science provider DrivenData to address this topic.

The objective is to use Machine Learning techniques to automate the chemical analysis procedure and speed up the drawing of essential results. Machine learning can also process large-scale datasets and create new analytical models. 

Each contender will create Machine Learning models to assist in analysing and interpreting data gathered from in-situ samples and laboratory instruments throughout the missions. This challenge is projected to increase the effectiveness of data analysis and accelerate future Mars missions.

You might also be interested in reading ‘Exploratory Data Analysis through Visualisation’ and ‘Understanding Data Science and Data Analysis Life Cycle’. 

Frequently Asked Questions

What role does Data Science play in space exploration?

Data Science in space exploration enables astronomers to process and analyse vast datasets from telescopes, satellites, and space missions. This capability allows them to make sense of complex astronomical events, identify patterns, and discover new celestial bodies, significantly advancing our understanding of the universe.

How does AI contribute to space technology?

AI in space technology automates the processing of astronomical data, enabling faster and more accurate analysis. It helps identify celestial objects, predict space weather, and optimise satellite operations. AI models, such as neural networks, enhance our ability to interpret data from space missions, leading to groundbreaking discoveries.

What are some examples of Data Science in astronomy? 

Examples of Data Science in astronomy include the Galaxy Zoo project, which classified 900,000 galaxy images using crowdsourcing and Data Science techniques. Neural networks analyse solar plasma motions, and Machine Learning models aid in discovering exoplanets from Kepler space telescope data, showcasing Data Science’s transformative impact on astronomical research.

Conclusion 

In conclusion, the above blog is a detailed research on my understanding of the importance and efficacy of Data Science in Space technology. Astronomers must process numerous volumes of data and conduct data analysis by creating their own ML models. 

With the help of Data Science in space exploration, astronomers are more curious to learn about the sun, Mars, and any other celestial planet. This is only possible when the volumes of datasets are carefully analysed. 

Suppose you want to undertake Data Science courses as part of your career choice and endure the field of astronomy. In that case, you must apply for the Data Science Job Guarantee Program by Pickl.AI. The course will allow you to not just learn from recorded videos but also practise on your own for hands-on experiences.