Role of AI in Modern Astronomy

Artificial intelligence (AI) is playing an increasingly vital role in modern astronomy, transforming how scientists handle data, make discoveries, and explore the universe.

Astronomy generates massive amounts of data, particularly with the advent of new, high-powered telescopes and surveys. AI helps process this data efficiently, often in real time, enabling astronomers to analyse information faster than with traditional methods.AI techniques, particularly Convolutional Neural Networks (CNNs) can identify celestial objects in telescope images by distinguishing galaxies, stars, and other phenomena from noise. This is especially useful for weak or faint signals that might otherwise be overlooked.

Astronomers are also using neural networks to dig deeper into the universe than ever before. Cosmologists are beginning to employ artificial intelligence to understand the fundamental nature of the cosmos. Two of the biggest cosmic mysteries are the identities of dark matter and dark energy, two substances beyond our current knowledge of physics that combined take up over 95% of all the energy contents throughout the universe.

Neural networks are aiding cosmologists in disentangling all the myriad effects of dark matter and dark energy. In this case, the training data comes from sophisticated computer simulations. In those simulations cosmologists vary the properties of dark matter and dark energy and see what changes. They then feed those results into the neural network so it can discover all the interesting ways that the universe changes. While not quite yet ready for primetime, the hope is that cosmologists could then point the neural network at real observations and allow it to tell us what the universe is made of.(space.com)

Projects like the Square Kilometre Array (SKA) and the Vera C. Rubin Observatory generate petabytes of data. AI algorithms streamline data collection and enable rapid sorting, making these large-scale surveys manageable and accessible for detailed analysis. (NASA)

The Vera C. Rubin Observatory as it takes shape in northern Chile. (Image credit: Rubin Obs/NSF/AURA)

AI is crucial for classifying galaxies, stars, and other astronomical objects. Machine learning algorithms trained on large datasets can identify specific features that help astronomers categorize objects.

AI models can sort galaxies by shape and structure, a traditionally labour-intensive process. Programs like Galaxy Zoo used public input to classify galaxies, and now machine learning can replicate that work on a much larger scale. Algorithms can scan through images, flagging new or changing light sources and alerting astronomers to potential supernovae, which can then be studied in detail.

Deep learning models have been integrated into detectors like LIGO and Virgo to distinguish gravitational wave signals from background noise, enabling real-time detection of these waves. AI’s signal extraction capabilities even helped identify a gravitational wave signal from two merging neutron stars in 2017. (astronomy.com)

AI-powered scheduling algorithms prioritize observation plans based on weather, visibility, and urgency. For example, the Gaia satellite uses AI-driven scheduling to determine optimal observation times, allowing for better tracking of transient events.

AI’s ability to detect anomalies is particularly useful in discovering previously unknown or rare phenomena. By identifying patterns and outliers in data that don’t match known objects, AI can flag potential new discoveries. Fast Radio Burst FRBs are mysterious, high-energy astrophysical phenomena, and their short duration makes them challenging to detect. AI algorithms scan radio data for FRB signals,helping astronomers catch these bursts in real time for further study.

Many modern telescopes are robotic and AI-controlled, enabling rapid, autonomous adjustments based on real-time data. This adaptability allows for better tracking of temporary phenomena and maximizes observation time.

Astronomers can also use AI to remove the optical interference created by Earth’s atmosphere from images of space taken by ground-based telescopes.

Eight images that show an AI software's process of "unblurring." To remove the effects of Earth's atmosphere from an image, the process pushes the starting image through eight layers of network, generating eight intermediate images. The earliest image is at top left, and the final image is at bottom right. (Image credit: Emma Alexander/Northwestern University)

AI enhances citizen science initiatives, which often rely on large numbers of volunteers to analyse images and classify objects.

For example:

-Galaxy Zoo: Originally, this project relied on public volunteers to classify galaxies. Now, machine learning models trained on that data can perform similar classifications, and the combination of human input and AI has greatly sped up the pace of research.

-SETI (Search for Extraterrestrial Intelligence): AI helps analyse radio signals for patterns that might indicate extra-terrestrial intelligence. By automating pattern recognition in massive datasets, AI enables more efficient and thorough searches. (pbs.com)

As AI has progressed, it has become an essential tool for astronomers. As telescopes get better, as data sets get larger and as AIs continue to improve, it is likely that this technology will play a central role in future discoveries about the universe. It is revolutionizing astronomy by managing vast amounts of data, accelerating discoveries, and allowing astronomers to delve deeper into the mysteries of the universe than ever before.

Written By:

Roshaneh Fatima

Glossary:

CNNs: A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. CNNs are particularly useful for finding patterns in images to recognize objects, classes, and categories.