Can Convolutional Neural Networks Find Alien Life?
Convolutional Neural Networks (CNNs) are an aspect of Artificial Intelligence (AI) that can analyze grid-like data such as images and video to spot patterns and recognize features using Deep Learning (DL) Algorithms.
Over the last two decades, the technology has become widely used in many different applications ranging from smartphones with facial recognition features to medical imaging and self-driving cars, but that only scratches the surface of the exciting potential for Convolutional Neural Networks.
At the tail end of 2021, NASA successfully launched the James Webb Telescope after years of delays, propelling our ability to observe deep space by exponential degrees with its ability to capture images with multiple layers of data for CNNs to process from galaxies 13 billion light-years away, suggesting new opportunities for us to discover extraterrestrial life.
The Search for Extraterrestrial Life
SETI, or the Search for Extraterrestrial Intelligence, is the academic pursuit of discovering alien life in our universe. It began with the advent of radio in the early 1900s and has become a focal point for astronomers and physicists, including Stephen Hawking, Nikola Tesla, and, more currently, Avi Loeb.
Some current methods for SETI include:
Radio Signal Analysis: A method of searching that uses massive radio telescopes to listen for patterns or other anomalies that can indicate intelligent life.
Optical SETI: Similar to a single lamp that uses Morse code to communicate via light, scientists use large telescopes to detect pulses of light that could be attempts at communication.
Astrobiology & Planetary Science: These scientific fields examine the geological aspects of a planet while also identifying planets within the habitable zone for carbon-based lifeforms.
Chemical Analysis: Astronomers use spectroscopy to analyze the atmosphere of planets, identifying chemical properties like oxygen or methane that can indicate possible signs of life.
Unfortunately, the vastness of space creates many challenges for scientists because of the immense amount of data that is recorded by their instruments. Signal interferences and time delays also factor into the problem, which can skew results and lead to inaccurate recordings. However, there are many ways that AI and CNNs can be used to mitigate this dilemma.
Role of AI in Astronomy
Due to the incredible amounts of data generated by astronomers, Machine Learning algorithms are becoming almost necessary to keep up with advanced astronomical technology. By feeding AI models consistent data feeds, these algorithms are helping astronomers discover new galaxies, planets, and more throughout the cosmos, helping us conceptualize major cosmic events:
Exoplanet Discovery: NASA’s Kepler Mission used AI to detect light curves around distant stars, which has led to the discovery of thousands of exoplanets (planets outside of a solar system).
Galaxy Classifications: AI can classify galaxies based on their shapes using images captured by telescopes, saving an extreme amount of time as there are 100-200 billion galaxies in the observable universe.
Gravitational Wave Detection: The Laser Interferometer Gravitational-Wave Observatory in Portugal used AI to detect gravitational waves rippling from cosmic events such as colliding black holes and neutron stars.
Detection of Fast Radio Bursts: Mysterious radio signal bursts, which generally have unknown origins, are becoming easier to detect using Machine Learning algorithms.
Supernova Identification: These powerful, luminous explosions from dying stars create large amounts of data that AI can use for crucial early detection.
The Power of Convolutional Neural Networks
Convolutional Neural Networks, a special type of neural network that analyzes grid-like data, are significant for astronomy because they can be used to analyze the massive images taken by the new James Webb telescope and its predecessor the Hubble Telescope.
Both telescopes can capture massive images that show hundreds, if not thousands, of galaxies dating back billions of light years in a single image. While this is not new for the Hubble telescope, the James Webb Telescope was fitted with additional camera lenses that can capture various details from across the electromagnetic spectrum.
By using feature learning, CNNs can analyze these incredible photographs to analyze radio signals, planet surfaces, and atmospheres across the universe. Their efficiency at analyzing images means that researchers can process information in a timely manner that would be impossible otherwise.
Future of CNNs in SETI
While alien life is something that we can only speculate about, there is no question that humans have been curious about extraterrestrial life for thousands of years and the advent of AI, and Convolutional Neural Networks specifically, could help us to discover new intelligent species.
The James Webb, which is scheduled to last 20 years, has already provided some incredible images of deep space, adding immense detail to already mind-boggling images taken by the Hubble Telescope. Using these images, Convolutional Neural Networks are one of the best tools available to process the gargantuan amount of stellar data being sent back to us from L2 orbit; and maybe, just maybe, it’ll spot something that changes our entire understanding of life.
