Artificial intelligence (AI) has become a vital tool for understanding the world. Now, as interest in space exploration regains steam, it could do the same for other worlds.
Despite decades of research, scientists still don’t know much about the universe beyond the Earth’s atmosphere. Mapping planets, stars, galaxies and their movements across space will help, but this has historically been a challenging task. AI makes it easier in several ways.
1. Identifying Celestial Bodies
With so much of the universe being so far away, it’s often hard to tell what’s what. Machine vision systems can help by distinguishing between different celestial bodies when doing so by the naked eye would be unreliable.
Researchers at the University of Hawaii developed a promising AI model for this purpose in 2020. Their neural network could identify galaxies with 98.1% accuracy and achieved 97.8% and 96.6% accuracy for stars and quasars, respectively. As a result, they were able to compile a 300-gigabyte catalog of the celestial bodies across three-quarters of the sky.
As telescopes and satellites provide more glimpses of the universe, classification algorithms like this could make it easier to interpret the data. Once scientists know what they’re looking at, they can conduct further research accurately.
2. Measuring Distance
Mapping the universe is not just a matter of knowing what’s out there. Researchers must also measure distances between bodies to understand scale and movement across galaxies or plan future satellite or spacecraft flights. AI helps in this area, too.
Calculating distance by monitoring celestial activity requires complex mathematical equations — the kind that AI is adept at. One solution achieved 76% accuracy in determining the size of and space between molecular clouds by analyzing radio telescope data. Another measured how far away gamma-ray bursts are by monitoring ground telescope feeds.
Tools like this provide more reliable estimates of the scale and speed of activity in the broader universe. As models improve with additional data, they could inform safer space travel or offer better insight into activity like solar flares or supernovas.
3. Understanding Skies in the Past
AI also helps map the universe by analyzing historical celestial movements. While there may not be detailed records on the skies throughout history, much of the visible sky is actually a representation of the past because of the time it takes light to travel. Consequently, scientists can use AI to model and understand previous trends in space.
One study built an AI model to create a catalog of over 17,000 galaxies near the Milky Way. Because it analyzed areas so far from Earth, the resulting simulations reflected the distant past, unveiling new information about how celestial bodies have moved across millennia. The resulting map included dark matter structures researchers didn’t know about before.
Such insights reveal how galaxies have shifted and impacted one another throughout history. Understanding that is key to better grasping how the universe works on a larger scale.
4. Predicting Future Shifts
AI can provide glimpses into the future as it learns about the past. Predictive analytics models have already applied the concept to sales forecasting and disease outbreak tracing, and they could do the same for major celestial events.
The AI solution that simulated historical galaxy movements revealed the Milky Way and Andromeda galaxies are moving toward each other. With additional data and a predictive model, scientists may be able to determine when a potential collision might occur in the distant future.
AI-driven predictions are particularly helpful on a smaller scale. Predictive analytics could help researchers predict solar storms or asteroid movements to know when action may be necessary to prevent damage or disruption on Earth.
5. Creating Accurate Planet Maps
Some AI tools take a narrower approach to mapping the universe. While understanding the stars is important, creating maps of physical planet surfaces may be more helpful in the near term, especially as space exploration picks up. NASA’s Mars Exploration rovers already use AI to navigate, and similar technologies could create detailed exoplanet surveys.
Making a geological map of a distant planet means combining a significant amount of data from different sources. That can be challenging, taking a lot of time and leaving substantial room for errors to occur. AI can compile the information to form a reliable single source of truth faster and with higher accuracy.
Machine learning can find trends and similarities in vast datasets humans may overlook. Consequently, it may be able to identify geological structures scientists would miss otherwise. These tools could also transform a pool of disparate data into a single, usable map to aid further exploration.
6. Speeding Mapping Timelines
Across all applications, AI mapping tools streamline the time it takes to analyze and understand the universe. That’s important because faster discovery leads to accelerated innovation and makes deeper research viable even in time-crunched environments.
A study from researchers in Japan highlights this potential. The team developed an AI simulator to model galaxies and dark matter distribution. It took just one CPU second on a laptop to run the simulation when it would normally take a supercomputer dozens of hours. Such speed came with no drop in accuracy, either.
When mapping processes take less time, scientists can perform more of them. As a result, the astronomy community can gain additional insight without time-consuming, expensive projects that may be harder to secure funding for.
7. Making Research More Accessible
Similarly, AI makes such research viable for a larger group of people. As timelines shrink, so do related costs. These optimizations mean smaller companies or less-funded researchers can partake in the same kind of study.
Conventional astronomy surveys can cost billions of dollars each — far more than many companies or educational institutions can afford. AI reduces those expenses in several ways. The most obvious is that it decreases the time it takes to conduct such research, but its benefits go further.
Complex machine learning models can deliver acceptable results with less data, lowering related costs. Ready-made algorithms are becoming more widely available as this technology grows, further reducing technology expenses and model training time. Trends like this will democratize space mapping, leading to a greater diversity of studies, which fosters innovation.
AI Is Unlocking the Secrets of the Universe
Scientists must map the universe with greater detail for the next wave of space exploration to take off. AI could be the key to that endeavor.
AI has already driven impressive improvements across a huge body of astronomy research. It will only get better as more teams embrace the technology — ideal parameters will become clearer and relevant data will grow. Machine learning could be the catalyst for the second space race amid this trend.
