machine learning Archives

November 20, 2024November 20, 2024 by Evan Gough

Main Sequence and White Dwarf Binaries are Hiding in Plain Sight

This ALMA image shows the binary HD101584. The pair of stars share a common envelope, and are surrounded by complex clouds of gas. Image Credit: By ALMA, CC BY 4.0, https://commons.wikimedia.org/w/index.php?curid=86644758

Some binary stars are unusual. They contain a main sequence star like our Sun, while the other is a “dead” white dwarf star that left fusion behind and emanates only residual heat. When the main sequence star ages into a red giant, the two stars share a common envelope.

This common envelope phase is a big mystery in astrophysics, and to understand what’s happening, astronomers are building a catalogue of main sequence-white dwarf binaries.

September 7, 2024September 7, 2024 by Laurence Tognetti

Artemis III Landing Sites Identified Using Mapping and Algorithm Techniques

Rendition of the 13 candidate landing site regions for NASA’s Artemis III mission, with each region measuring approximately 15 by 15 kilometers (9.3 by 9.3 miles). Final landing sites within those regions measure approximately 200 meters (656 feet) across. (Credit: NASA)

Where would be the most ideal landing site for the Artemis III crew in SpaceX’s Human Landing System (HLS)? This is what a recent study submitted to Acta Astronautica hopes to address as an international team of scientists investigated plausible landing sites within the lunar south pole region, which comes after NASA selected 13 candidate landing regions in August 2022 and holds the potential to enable new methods in determining landing sites for future missions, as well.

August 5, 2024August 5, 2024 by Evan Gough

Fast-Tracking the Search for Habitable Worlds

Astronomers have detected thousands of planets, including dozens that are potentially habitable. To winnow them down, they need to understand their atmospheres and other factors. (NASA Illustration)

Modern astronomy would struggle without AI and machine learning (ML), which have become indispensable tools. They alone have the capability to manage and work with the vast amounts of data that modern telescopes generate. ML can sift through large datasets, seeking specified patterns that would take humans far longer to find.

The search for biosignatures on Earth-like exoplanets is a critical part of contemporary astronomy, and ML can play a big role in it.

May 31, 2024May 31, 2024 by Laurence Tognetti

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.

May 2, 2024May 2, 2024 by Matt Williams

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.

March 11, 2024March 11, 2024 by Brian Koberlein

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.

January 22, 2024January 22, 2024 by Evan Gough

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.

January 16, 2024January 16, 2024 by Nancy Atkinson

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.

September 23, 2023September 23, 2023 by Scott Alan Johnston

Machine Learning Algorithms Can Find Anomalous Needles in Cosmic Haystacks

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.

August 22, 2023August 22, 2023 by Matt Williams

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.