The face of astronomy is changing. Though narrow-field point-and-shoot astronomy still matters (JWST anyone?), large wide-field surveys promise to be the powerhouses of discovery in the coming decades, especially with the advent of machine learning.
A recently developed machine learning program, called ASTRONOMALY, scanned nearly four million galaxy images from the Dark Energy Camera Legacy Survey (DECaLS), discovering 1635 anomalies including 18 previously unidentified sources with “highly unusual morphology.” It is a sign of things to come: a partnership between humans and software that can do better observational science than either could do on their own.
Along with still operational missions, these observatories will gather massive volumes of high-resolution spectroscopic data. Sorting through this data will require cutting-edge machine-learning techniques to look for indications of life and biological processes (aka. biosignatures). In a recent paper, a team of scientists from the Institute for Fundamental Theory at the University of Florida (UF-IFL) recommended that future surveys use machine learning to look for anomalies in the spectra, which could reveal unusual chemical signatures and unknown biosignatures.
In 2017, humanity got its first glimpse of an interstellar object (ISO), known as 1I/’Oumuamua, which buzzed our planet on its way out of the Solar System. Speculation abound as to what this object could be because, based on the limited data collected, it was clear that it was like nothing astronomers had ever seen. A controversial suggestion was that it might have been an extraterrestrial probe (or a piece of a derelict spacecraft) passing through our system. Public fascination with the possibility of “alien visitors” was also bolstered in 2021 with the release of the UFO Report by the ODNI.
This move effectively made the study of Unidentified Aerial Phenomena (UAP) a scientific pursuit rather than a clandestine affair overseen by government agencies. With one eye on the skies and the other on orbital objects, scientists are proposing how recent advances in computing, AI, and instrumentation can be used to assist in the detection of possible “visitors.” This includes a recent study by a team from the University of Strathclyde that proposes how hyperspectral imaging paired with machine learning could lead to an advanced data pipeline for characterizing UAP.
When measuring distances in the Universe, astronomers rely on what is known as the “Distance Ladder” – a succession of methods by which distances are measured to objects that are increasingly far from us. But what about age? Knowing with precision how old stars, star clusters, and galaxies are is also paramount to determining how the cosmos has evolved. Thanks to a new machine learning technique developed by researchers from Keele University, astronomers may have established the first rung on a “cosmic age ladder.”
There are plenty of places for life to hide. Even on our blue planet, where we know there is abundant life, it is sometimes difficult to predict all the different environments it might crop up in. Exploring worlds other than our own for life would make it exponentially more difficult to detect it because, realistically, we don’t really know what we’re looking for. But life will probably present itself with some sort of pattern. And there is one new technology that is exceptional at detecting patterns: machine learning. Researchers at the SETI Institute have started working on a machine-learning-based AI system that will do just that.
Astronomy has entered the era of big data, where astronomers find themselves inundated with information thanks to cutting-edge instruments and data-sharing techniques. Facilities like the Vera Rubin Observatory (VRO) are collecting about 20 terabytes (TB) of data on a daily basis. Others, like the Thirty-Meter Telescope (TMT), are expected to gather up to 90 TB once operational. As a result, astronomers are dealing with 100 to 200 Petabytes of data every year, and astronomy is expected to reach the “exabyte era” before long.
In response, observatories have been crowdsourcing solutions and making their data open-access so citizen scientists can assist with the time-consuming analysis process. In addition, astronomers have been increasingly turning to machine learning algorithms to help them identify objects of interest (OI) in the Universe. In a recent study, a team led by the University of Georgia revealed how artificial intelligence could distinguish between false positives and exoplanet candidates simultaneously, making the job of exoplanet hunters that much easier.
For over sixty years, astronomers and astrophysicists have been engaged in the Search for Extraterrestrial Intelligence (SETI). This consists of listening to other star systems for signs of technological activity (or “technosignatures), such as radio transmissions. This first attempt was in 1960, known as Project Ozma, where famed SETI researcher Dr. Frank Drake (father of the Drake Equation) and his colleagues used the radio telescope at the Green Bank Observatory in West Virginia to conduct a radio survey of Tau Ceti and Epsilon Eridani.
Since then, the vast majority of SETI surveys have similarly looked for narrowband radio signals since they are very good at propagating through interstellar space. However, the biggest challenge has always been how to filter out radio transmissions on Earth – aka. radio frequency interference (RFI). In a recent study, an international team led by the Dunlap Institute for Astronomy and Astrophysics (DIAA) applied a new deep-learning algorithm to data collected by the Green Bank Telescope (GBT), which revealed eight promising signals that will be of interest to SETI initiatives like Breakthrough Listen.
We’ve reported on Gaia’s incredible data-collection abilities in the past. Recently, it released DR3, its latest data set, with over 1.8 billion objects in it. That’s a lot of data to sift through, and one of the most effective ways to do so is through machine learning. A group of researchers did just that by using a supervised learning algorithm to classify a particular type of object found in the data set. The result is one of the world’s most comprehensive catalogs of the type of astronomical object known as variables.
Astronomers have been assessing a new machine learning algorithm to determine how reliable it is for finding gravitational lenses hidden in images from all sky surveys. This type of AI was used to find about 5,000 potential gravitational lenses, which needed to be confirmed. Using spectroscopy for confirmation, the international team has now determined the technique has a whopping 88% success rate, which means this new tool could be used to find thousands more of these magical quirks of physics.