machine learning Archives

August 18, 2023August 22, 2023 by Matt Williams

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.

July 13, 2023July 13, 2023 by Matt Williams

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.”

April 14, 2023April 14, 2023 by Alan Boyle

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)

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.

March 14, 2023March 14, 2023 by Andy Tomaswick

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.

February 10, 2023February 10, 2023 by Matt Williams

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.

February 10, 2023March 6, 2023 by Matt Williams

More Data and Machine Learning has Kicked SETI Into High Gear

Artist’s impression of Green Bank Telescope connected to a machine learning network. Credit: Breakthrough Listen/Danielle Futselaar.

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.

December 6, 2022December 6, 2022 by Andy Tomaswick

Not Just Stars. Gaia Mapped a Diverse and Shifting Universe of Variable Objects

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.

September 30, 2022September 30, 2022 by Nancy Atkinson

A Computer Algorithm is 88% Accurate in Finding Gravitational Lenses

Pictures of gravitational lenses from the AGEL survey. Credit: ARC Centre of Excellence for All Sky Astrophysics in 3-Dimensions (ASTRO3D) and the University of NSW (UNSW).

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.

April 7, 2022April 7, 2022 by Matt Williams

If Aliens Were Sending us Signals, This is What They Might Look Like

For over sixty years, scientists have been searching the cosmos for possible signs of radio transmission that would indicate the existence of extraterrestrial intelligence (ETI). In that time, the technology and methods have matured considerably, but the greatest challenges remain. In addition to having never detected a radio signal of extraterrestrial origin, there is a wide range of possible forms that such a broadcast could take.

In short, SETI researchers must assume what a signal would look like, but without the benefit of any known examples. Recently, an international team led by the University of California Berkeley and the SETI Institute developed a new machine learning tool that simulates what a message from extraterrestrial intelligence (ETI) might look like. It’s known as Setigen, an open-source library that could be a game-changer for future SETI research!

March 19, 2022March 19, 2022 by Matt Williams

Machine Learning Will be one of the Best Ways to Identify Habitable Exoplanets

Illustration of Kepler-186f, a recently-discovered, possibly Earthlike exoplanet that could be a host to life. (NASA Ames, SETI Institute, JPL-Caltech, T. Pyle) — This is Kepler 186f, an exoplanet in the habitable zone around a red dwarf. We've found many planets in their stars' habitable zones where they could potentially have surface water. But it's a fairly crude understanding of true habitability. Image Credit: NASA Ames, SETI Institute, JPL-Caltech, T. Pyle)

The field of extrasolar planet studies is undergoing a seismic shift. To date, 4,940 exoplanets have been confirmed in 3,711 planetary systems, with another 8,709 candidates awaiting confirmation. With so many planets available for study and improvements in telescope sensitivity and data analysis, the focus is transitioning from discovery to characterization. Instead of simply looking for more planets, astrobiologists will examine “potentially-habitable” worlds for potential “biosignatures.”

This refers to the chemical signatures associated with life and biological processes, one of the most important of which is water. As the only known solvent that life (as we know it) cannot exist, water is considered the divining rod for finding life. In a recent study, astrophysicists Dang Pham and Lisa Kaltenegger explain how future surveys (when combined with machine learning) could discern the presence of water, snow, and clouds on distant exoplanets.