This article was published as part of the Data Science Blogathon.
Overview
Space exploration has always been of interest to scientists and governments around the world, as it contains the key to the origin of humanity and many wonderful wonders of the universe, including the possibility of alien lives. The visible universe represents the parts of space that we can see using telescopes. But nevertheless, scientists and explorers believe the universe may be bigger than that.
Till the date, scientists have roughly explored only the 4% of the visible universe that is made up of planets, stars, galaxies and other astronomical objects that astronomers and scientists can see and know about. The rest 96% still unexplored.
Understanding Artificial Intelligence and Machine Learning
Artificial Intelligence or the shorter and cooler term AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.. The term can also be applied to any machine that exhibits traits associated with a human mind., like learning and problem solving.
Machine learning is a specialized branch in the domain of artificial intelligence that deals with training machines to develop intelligence that allows them to perform complex tasks using their intelligence. Machine learning algorithms use tons of data to help machines become familiar with the various scenarios they may face.. Allows machines to learn from your training experience and use them in real life scenarios.
TO THE / ML in space exploration
Now, if we were to combine the ideas of these two massive terms, namely, AI and space exploration, taking into account recent developments in the field of machine learning and artificial intelligence, imagine how easy it would be for scientists and explorers to achieve their goal and how it would affect our lives.
Let's put these two ideas together and see what has already been done, what's going on and what else could be done.
1. First image of a black hole
We obtained our first black hole image using the CHIRP algorithm (Continuous reconstruction of high resolution images using Patch Priors). CHIRP is a Bayesian algorithm used to perform deconvolution on images created in radio astronomy. The development of CHIRP involved a large team of Computer Science and Artificial Intelligence researchers from MIT. CHIRP used the image data from the Event Horizon telescopes that were too large and this is where the image processing had to be done.. Scientists used Numpy, pandas and other python libraries to reduce data, data correlation and calibrations. ML was also used in image analysis.
Check this link for more details: https://numpy.org/case-studies/blackhole-image/
Now that we have the first image of a black hole, scientists and researchers are working to obtain more accurate images of a black hole. To do it, create more complex algorithms that will use more machine learning and artificial intelligence.
Note that many objects are still unknown to us in deep space, so the application of Machine Learning and Deep Learning will help us to classify the type of object and these investigations in the future may lead to identify more and more new objects and, Thus, help scientists. and explorers in space exploration.
2. Assistants and robots based on artificial intelligence
Do the names TARS and CASE ring a bell?? Yes, I'm talking about the robots from the famous movie 'Interstellar’ (and in case you haven't seen it, I recommend you do it). If you remember the role of TARS and CASE in the movie, imagine how useful they would be in helping astronauts in real life.
Scientists are developing AI-based assistants to help astronauts on their mission to the Moon, Mars and beyond. These assistants are designed to understand and predict crew requirements and understand astronauts' emotions and mental health and take necessary actions in the event of an emergency. Now, how do they do that? The answer to this is sentiment analysis.. Sentiment analysis (also known as opinion mining or emotion AI) is a subfield of NLP (natural language processing) that tries to identify and extract opinions within a given text through blogs, reviews, social media, forums, news, etc.
The robots, Secondly, can be more helpful when it comes to physical assistants, how to help pilot spaceships, dock spacecraft and handle extreme conditions that are unsafe for humans. Most of it may seem hypothetical, but it will be of great help to astronauts.
3. Smart navigation system
In 2018, NASA with the help of Intel developed an artificial intelligence system that helped astronauts find their way around the planets. This new navigation system will help to easily navigate the surface of the planets through the shortest possible routes.. Scientists applied this program to our moon and the way this system worked was that it simulated the surface of the moon and then compared it to the local environment. The AI would train on the millions of images of the moon and then use a neural network to create a map of the virtual moon.. The same algorithm was later applied to the Mars exploration program.
4. Kepler's exoplanets discovery
NASA's Kepler telescope was designed to determine the frequency of Earth-sized planets orbiting stars similar to the Sun., but these planets were on the verge of mission detection sensitivity. The precise determination of the rate of occurrence of these planets required an automatic and precise assessment of the probability that the individual candidates are actually planets., even with a low signal ratio / noise.
To overcome this limitation, Google researchers and other scientists used a convolutional neural network called AstroNet K2 to predict whether a given signal from the Kepler space telescope is a transiting exoplanet or a false positive caused by astrophysical or instrumental phenomenon.. By training this neural network model up to a 98 (about) percent, successfully identified two new exoplanets, namely, Kepler 80g and Kepler 90i, orbiting the Kepler star system 80 and the Kepler star system 90, respectively.
5. Space debris solution
Space debris around the Earth (SOURCE: NASA)
Have you ever thought about what happens to satellites and spaceships that are sent into space and never return to Earth?, good, those turn into space junk. Space junk or space junk is any piece of machinery or junk left by humans in space. May refer to large objects such as dead satellites that have failed or have been left in orbit at the end of their mission.
The image you see above was released on 2013 by NASA and showed the amount of space debris we had in 2013. The problem with space debris has reached a critical point as scientists and researchers continue to send satellites into space., that never takes place. back. There is more of 23,000 human-made fragments in space that are larger than 4 "and more of 500,000 small particles. The real concern with these space debris is that when they collide with satellites or the spacecraft, leave a dent in the body which sometimes becomes the main reason for space accidents.
To overcome this problem, Scientists are using deep learning to improve the precision of traditionally used laser range technology. They used backpropagation neural network models to identify the location of the debris. It was also mentioned that after improving the pointing precision of the telescope through a deep learning technique, space debris with a cross-sectional area of 1 square meter and a distance of 1500 kilometers can be accurately identified.
panorama
There are many other investigations underway on the implementation of artificial intelligence in space exploration. Even if, like other AI applications, nothing can be certain and concrete. At the end of the day, we need human intervention in everything that AI is capable of doing. With every innovation, AI is getting closer to providing newer knowledge and proving to be an advantage for humans in interstellar space exploration with machine and innovative project and research.
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