Artificial intelligence (AI) has become a key tool for understanding the world, and we’re currently seeing a resurgence of interest in space exploration, with the potential to have a similar effect on other worlds.
Despite decades of research, scientists still know very little about the universe beyond Earth’s atmosphere. Mapping planets, stars, and galaxies and their movements through space would be useful, but this has historically been a difficult task. AI can make it easier in several ways.
1. Identifying celestial objects
Much of the universe is so far away that it’s often difficult to tell what’s what, and machine vision systems help us distinguish between different celestial objects that the naked eye can’t rely on.
Researchers at the University of Hawaii developed a promising AI model for this purpose in 2020. Their neural network Identifies galaxies with 98.1% accuracy The team achieved accuracies of 97.8% and 96.6% for stars and quasars, respectively, resulting in a 300-gigabyte catalog of celestial objects covering three-quarters of the sky.
As telescopes and satellites reveal more about the universe, classification algorithms like these could help scientists interpret the data — knowing what they’re looking at could help them study it more precisely.
2. Measuring Distance
Mapping the universe isn’t just about knowing what’s out there. Researchers also need to measure the distances between celestial objects to understand the scale and motion between galaxies, and to plan future satellite and spacecraft missions. AI can help here, too.
Monitoring celestial activity and calculating distances requires complex mathematical formulas, something AI excels at. One solution is Achieved 76% accuracy A study to determine the size and spacing of molecular clouds by analyzing data from radio telescopes, and to measure how far away gamma-ray bursts are by monitoring images from ground-based telescopes.
Such tools provide more reliable estimates of the scale and rate of activity across the universe. As additional data improves models, they can inform safer space travel and provide deeper insights into activity such as solar flares and supernovae.
3. Understanding the sky of the past
AI can also help map the universe by analyzing the past movements of celestial objects. While we may not have kept detailed records of the sky throughout history, given the time it takes light to travel, much of the visible sky is actually from the past. Scientists can therefore use AI to model and understand the past trends of the universe.
In one study, we built an AI model to A catalogue of over 17,000 galaxies Near the Milky Way galaxy. Because they analyzed a region far from Earth, the resulting simulations reflected the distant past and revealed new information about how celestial objects have moved over millennia. The resulting map included a previously unknown structure of dark matter, which the researchers found to be the source of the dark matter.
Such insights shed light on how galaxies have moved and influenced each other throughout history, and understanding that is key to better understanding how the universe works on a broader scale.
4. Anticipate future changes
AI can glimpse the future by studying the past: predictive analytics models are already applying this concept to forecasting sales and tracking disease outbreaks, and could do the same for major celestial events.
An AI solution simulating the past movements of galaxies has revealed that the Milky Way and Andromeda galaxies are moving closer to each other, and with additional data and predictive models, scientists may be able to determine when a collision could occur in the distant future.
AI-powered predictions are useful, especially on a smaller scale: predictive analytics can help researchers forecast solar storms and asteroid movements, letting them know when action is needed to prevent damage and disruption to Earth.
5. Creating accurate planetary maps
Some AI tools take a narrow approach to mapping the universe: While understanding the stars is important, mapping the physical planetary surfaces may be more useful in the short term, especially as space exploration picks up steam. For example, NASA’s Mars Exploration Rovers. We’re already using AI to navigateSimilar techniques will enable detailed studies of exoplanets.
Creating a geological map of a distant planet requires combining large amounts of data from various sources — a difficult task that takes time and leaves a lot of room for error. AI can bring information together to create a trusted single source of truth faster and more accurately.
Machine learning can find trends and similarities in huge data sets that humans might miss, potentially identifying geological structures that scientists would miss. These tools can also turn disparate pools of data into a single usable map for further investigation.
6. Speeding up mapping timelines
Across all applications, AI mapping tools streamline the time it takes to analyze and understand the universe, which is important because faster discoveries lead to faster innovation and enable deeper research in time-pressured environments.
A study by Japanese researchers highlights this possibility: They developed an AI simulator to model the distribution of galaxies and dark matter. It took only 1 CPU second. Simulations that would normally take tens of hours on a supercomputer can now be run on a laptop, without any loss of accuracy at these speeds.
Reducing the time required for the mapping process allows scientists to map more, thereby providing the astronomy community with additional insights without the need for lengthy and expensive projects that can be difficult to fund.
7. Making research more accessible
Similarly, AI makes such research feasible for more people. As timelines shrink, so do the associated costs. These optimizations allow smaller companies and researchers with fewer resources to participate in similar research.
Traditional astronomy surveys It could cost billions of dollars Each of these costs is far beyond what most businesses and educational institutions can afford. AI reduces these costs in several ways. The most obvious is that it reduces the time it takes to conduct such research, but that’s not the only benefit.
Complex machine learning models can provide acceptable results with less data, reducing associated costs. As this technology matures, off-the-shelf algorithms will become more widely available, further reducing technology costs and model training times. Trends like these democratize space mapping, increasing research diversity and spurring innovation.
AI unlocks the secrets of the universe
Scientists need to map the universe in greater detail to successfully launch the next wave of space exploration, and AI could be key to that effort.
AI has already made impressive advances in the vast field of astronomy research, and as more teams adopt the technology, ideal parameters become clearer and more data becomes available, AI will only get better. Amid this trend, machine learning could be the catalyst for a second space race.
