Extra-terrestrial life exists beyond Earth. Can artificial intelligence discover it?
Machine learning, the branch of artificial intelligence that enables computer systems to learn from data, has made significant strides in the field of astronomy, particularly in the search for exoplanets. This technology has automated and enhanced the classification and detection of exoplanet candidates from large datasets, such as those from NASA's Kepler mission.
One notable example is the machine learning program ExoMiner, developed by Hamed Valizadegan. Launched in 2018, ExoMiner has been instrumental in speeding up exoplanet-hunting efforts. Although it is often referred to as a black box, ExoMiner has successfully identified 370 previously unknown exoplanets. However, none of these new planets are similar to Earth or any other planet in our solar system.
Another groundbreaking development comes from the collaboration between Lisa Kaltenegger, an exoplanet astrophysicist, and Dang Pham. They have trained machine learning systems to pinpoint life-enabling resources like water. Their algorithm, while not providing absolute certainty, can estimate that some percentage of a planet's surface is covered with life. In simulated tests, it was able to detect the existence of life about three-quarters of the time.
The algorithm shows particular promise in spotting the telltale signs of leafy plants, but it is less reliable when looking for evidence of lichen, tree bark, or biofilm. Despite this, it serves as a helpful clue in the initial hunt for another Earth. Human scientists will still need to point more telescopes towards the planet and look for chemical signatures that could indicate life is there.
The quest for finding Earthlike planets is an astronomical problem due to the vast number of planets in the Milky Way. However, advancements in AI are offering new possibilities. For instance, Lia Medeiros, a computational astrophysicist and a member of the Event Horizon Telescope team, has developed an algorithm called PRIMO. This algorithm creates a new, higher definition image of a black hole. In the future, it could be used to construct images of other mysterious objects, potentially bringing previously invisible stages of planet formation into view.
One of the key benefits of using AI in astronomy is increased efficiency and scalability. Machine learning models can process vast volumes of stellar light curve data faster than manual or classical methods, enabling quicker identification of exoplanet candidates. Furthermore, techniques like vision transformers capture intricate temporal patterns in light curves, improving classification performance.
However, there are notable limitations and challenges. Data imbalance and quality can constrain machine learning performance, and ensuring that models trained on existing missions like Kepler generalize well to new data or missions remains an active area of research. Interpretability is another issue, as some AI models, especially deep learning architectures, can be "black boxes," making it difficult to explain or validate their findings fully.
Despite these challenges, ongoing research aims to optimize model architectures and enhance automation while addressing these constraints. The Vera C. Rubin Observatory in Chile, to be operational in 2025, will image the whole sky every three nights with a resolution of 3,200 megapixels and is expected to capture data on one million supernovae every year, as well as tens of thousands of asteroids and other celestial objects.
The story of Hamed Valizadegan, who grew up in Iran with a love for the night sky and a curiosity about the transient nature of life, humanity's place in the universe, and the forward motion of time, serves as a testament to the enduring allure of the stars. His desire to study the stars beyond Earth led him to team up with astronomer Jon Jenkins in 2014 to join a more automated search for another Earthlike planet in our galaxy.
AI could reveal something different in the search for Earthlike planets due to its ability to take a deeper look. As we continue to push the boundaries of what is possible with machine learning, the future of exoplanet hunting looks brighter than ever.
- Although ExoMiner, a machine learning program established by Hamed Valizadegan, is often considered a black box, it has successfully identified 370 previously unknown exoplanets, contributing significantly to the search for Earth-like planets.
- Machine learning, through increased efficiency and scalability, has revolutionized the field of astronomy by enabling quicker identification of exoplanet candidates from vast datasets, such as those from NASA's Kepler mission.
- Lisa Kaltenegger and Dang Pham's collaboration in training machine learning systems to pinpoint life-enabling resources like water demonstrates the potential of AI in spotting potential Earth-like planets.
- In the future, algorithms like Lia Medeiros' PRIMO could be used to construct images of mysterious celestial objects, potentially bringing previously invisible stages of planet formation into view.
- The story of Hamed Valizadegan, whose love for the night sky and curiosity about the universe, serves as a testament to the enduring allure of the stars and the boundless possibilities that artificial intelligence and our understanding of space-and-astronomy hold for the future of exoplanet hunting.
