How Machine Learning Algorithms Help Scientists Space Exploration

Machine Learning Algorithms Help Scientists Explore Mars

Planet Mars has been a subject generating a significant amount of interest. Scientist all over the World wants to explore it as much as they can. Why? The reason is, they want to know its history as well as its present status pertaining to its environment. 

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You might be wondering, what is so special about it. There are so many planets around, why do we have to emphasize Mars so much. Is there any possibility of life on Mars? Can we colonize Mars in the long run? Numerous questions are there. We will come to know more about Mars with the passage of time. More layers of information related to Mars will unveil in the time ahead.

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This article deals with Machine Learning while exploring Mars. Mars is on our radar therefore, mankind is taking every possible initiative to explore Mars. Keeping in mind this point, we have sent quite a few missions to Mars. The idea is to explore Mars without leaving any stone unturned. It's important because it is widely believed Mars was habitable in the past and was a watery world. This has generated a lot of interest among the scientists. Maybe Mars had facilitated life-supporting chemistry in the past which makes it a very interesting place to channel our effort and focus on it.

How Machine Learning Algorithms Used in Space Exploration?

Machine Learning algorithms are also being used as a tool to study more about Mars. If you are hearing this term for the first time then you are bound to think, what exactly are machine learning algorithms? It basically means utilizing the Artificial Intelligence System to carry out its tasks so that output values can be predicted from the input values.

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A new article published in Earth and Space Science discusses the data collected by Curiosity's Chemistry and Camera (ChemCam). ChemCam uses the services of two instruments: a laser-induced breakdown spectrometer as well as a Remote Micro-Imager. 

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Curiosity landed in Gale Crater in 2012 and since then ChemCam has been successful in collecting individual spectra in excess of 800,000 from more than 2500 samples. It still remains a challenge to use Machine Learning to examine Chemcam's data because of a lack of data sets from Mars, according to Rammelkamp and colleagues.

Machine Learning algorithms can be used effectively as a potent and powerful tool to map Mar's surface. Researchers have applied Machine Learning algorithms on several chemical compositions of Mars and have opined such devices could be efficient and effective in mapping the surface of Mars.

With the advancement of NASA's Mars rovers, it is getting strongly evident that Artificial Intelligence backed technologies and Machine learning will make them more competitive, dynamic and impressive, as they travel planet Mars in their endeavor to study the planet while looking for valuable clues. 

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Mars Express Orbiter & Mars Rovers have collected data from Mars and the same has been analyzed. The autonomy used in such exploration spacecraft as well as on Earth to examine data collected by such vehicles revolved around Machine Learning. Apart from these, Machine Learning could be of immense value when it comes to studying the data of Mars with respect to its climate, atmosphere and potential future habitation.

Conclusion

Sending astronauts to Mars will involve risk and by the time we manage to send astronauts to Mars, we need ways to explore the surface of Mars. Machine Learning tools can help us in a colossal way to understand Mars. It has enough potential to provide us with methodologies to analyze data collected from Mars and present a picture that is scientifically valid. In times ahead, Machine Learning will be used extensively for such explorations. 

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