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.

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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.

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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.

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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.

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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.

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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.

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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.”

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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.

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Finding Life in the Solar System Means Crunching a Lot of Data. The Perfect Job for Machine Learning

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.

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Machine Learning is a Powerful Tool When Searching for Exoplanets

Three young planets in orbit around an infant star known as HD 163296 Credit: NRAO/AUI/NSF; S. Dagnello

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.

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