A New Deep Learning Algorithm Can Find Earth 2.0

Artist's impression of Proxima Centauri b, which orbits Alpha Centauri C in the triple-star system, Alpha Centauri. (Credit: ESO/M. Kornmesser)

How can machine learning help astronomers find Earth-like exoplanets? This is what a recently accepted study to Astronomy & Astrophysics hopes to address as a team of international researchers investigated how a novel neural network-based algorithm could be used to detect Earth-like exoplanets using data from the radial velocity (RV) detection method. This study holds the potential to help astronomers develop more efficient methods in detecting Earth-like exoplanets, which are traditionally difficult to identify within RV data due to intense stellar activity from the host star.

Continue reading “A New Deep Learning Algorithm Can Find Earth 2.0”

What Can AI Learn About the Universe?

Will AI become indispensable in an age of "big data" astronomy? Credit: DALL-E

Artificial intelligence and machine learning have become ubiquitous, with applications ranging from data analysis, cybersecurity, pharmaceutical development, music composition, and artistic renderings. In recent years, large language models (LLMs) have also emerged, adding human interaction and writing to the long list of applications. This includes ChatGPT, an LLM that has had a profound impact since it was introduced less than two years ago. This application has sparked considerable debate (and controversy) about AI’s potential uses and implications.

Astronomy has also benefitted immensely, where machine learning is used to sort through massive volumes of data to look for signs of planetary transits, correct for atmospheric interference, and find patterns in the noise. According to an international team of astrophysicists, this may just be the beginning of what AI could do for astronomy. In a recent study, the team fine-tuned a Generative Pre-trained Transformer (GPT) model using observations of astronomical objects. In the process, they successfully demonstrated that GPT models can effectively assist with scientific research.

Continue reading “What Can AI Learn About the Universe?”

Black Holes Need Refreshing Cold Gas to Keep Growing

A pair of disc galaxies in the late stages of a merger. Credit: NASA

The Universe is filled with supermassive black holes. Almost every galaxy in the cosmos has one, and they are the most well-studied black holes by astronomers. But one thing we still don’t understand is just how they grew so massive so quickly. To answer that, astronomers have to identify lots of black holes in the early Universe, and since they are typically found in merging galaxies, that means astronomers have to identify early galaxies accurately. By hand. But thanks to the power of machine learning, that’s changing.

Continue reading “Black Holes Need Refreshing Cold Gas to Keep Growing”

Vera Rubin Will Help Us Find the Weird and Wonderful Things Happening in the Solar System

The Vera Rubin Observatory at twilight on April 2021. It's been a long wait, but the observatory should see first light later this year. Image Credit: Rubin Obs/NSF/AURA

The Vera Rubin Observatory (VRO) is something special among telescopes. It’s not built for better angular resolution and increased resolving power like the European Extremely Large Telescope or the Giant Magellan Telescope. It’s built around a massive digital camera and will repeatedly capture broad, deep views of the entire sky rather than focus on any individual objects.

By repeatedly surveying the sky, the VRO will spot any changes or astronomical transients. Astronomers call this type of observation Time Domain Astronomy.

Continue reading “Vera Rubin Will Help Us Find the Weird and Wonderful Things Happening in the Solar System”

Machine Learning Could Find all the Martian Caves We Could Ever Want

Examples of potential cave entrances (PCEs) on Mars and their assigned category from the Mars Global Candidate cave Catalogue (MGC3). Credit: NASA/JPL/MSSS/The Murray Lab.

The surface of Mars is hostile and unforgiving. But put a few meters of regolith between you and the Martian sky, and the place becomes a little more habitable. Cave entrances from collapsed lava tubes could be some of the most interesting places to explore on Mars, since not only would they provide shelter for future human explorers, but they could also be a great place to find biosignatures of microbial life on Mars.

But cave entrances are difficult to spot, especially from orbit, as they blend in with the dusty background. A new machine learning algorithm has been developed to quickly scan images of the Martian surface, searching for potential cave entrances.

Continue reading “Machine Learning Could Find all the Martian Caves We Could Ever Want”

Machine Learning Algorithms Can Find Anomalous Needles in Cosmic Haystacks

ESA/Webb, NASA & CSA, J. Rigby.

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.

Continue reading “Machine Learning Algorithms Can Find Anomalous Needles in Cosmic Haystacks”

The Most Compelling Places to Search for Life Will Look Like “Anomalies”

Will it be possible someday for astrobiologists to search for life "as we don't know it"? Credit: NASA/Jenny Mottar

In the past two and a half years, two next-generation telescopes have been sent to space: NASA’s James Webb Space Telescope (JWST) and the ESA’s Euclid Observatory. Before the decade is over, they will be joined by NASA’s Nancy Grace Roman Space Telescope (RST), Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx), and the ESA’s PLAnetary Transits and Oscillations of stars (PLATO) and ARIEL telescopes. These observatories will rely on advanced optics and instruments to aid in the search and characterization of exoplanets with the ultimate goal of finding habitable planets.

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.

Continue reading “The Most Compelling Places to Search for Life Will Look Like “Anomalies””

Want to Find UFOs? That's a Job for Machine Learning

UFO encounter video
Cockpit video shows an anomalous aerial encounter in 2015. Credit: U.S Navy Video

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.

Continue reading “Want to Find UFOs? That's a Job for Machine Learning”

How Old is That Star? Ask a Computer

An open cluster of stars known as IC 4651, a stellar grouping that lies at in the constellation of Ara (The Altar). Credit: ESO

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.”

Continue reading “How Old is That Star? Ask a Computer”

Artificial Intelligence Produces a Sharper Image of M87’s Big Black Hole

The new PRIMO reconstruction of the black hole in M87. This is based on a newly "cleaned-up" image from the Event Horizon Telescope. (Credit: Lia Medeiros et al. / ApJL, 2023)
The new PRIMO reconstruction of the black hole in M87. This is based on a newly "cleaned-up" image from the Event Horizon Telescope. (Credit: Lia Medeiros et al. / ApJL, 2023)

Astronomers have used machine learning to sharpen up the Event Horizon Telescope’s first picture of a black hole — an exercise that demonstrates the value of artificial intelligence for fine-tuning cosmic observations.

The image should guide scientists as they test their hypotheses about the behavior of black holes, and about the gravitational rules of the road under extreme conditions.

Continue reading “Artificial Intelligence Produces a Sharper Image of M87’s Big Black Hole”