machine learning Archives

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. Scientists could use this one or one like it to measure planetary entropy production as a prelude to exploration. (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.

November 26, 2021November 26, 2021 by Matt Williams

A Machine-Learning Algorithm Just Found 301 Additional Planets in Kepler Data

Artist's concept of the Kepler mission with Earth in the background. Credit: NASA/JPL-Caltech

Looking to the future, astronomers are excited to see how machine learning – aka. deep learning and artificial intelligence (AI) – will enhance surveys. One field that is already benefitting in the search for extrasolar planets, where researchers rely on machine-learning algorithms to distinguish between faint signals and background noise. As this field continues to transition from discovery to characterization, the role of machine intelligence is likely to become even more critical.

Take the Kepler Space Telescope, which accounted for 2879 confirmed discoveries (out of the 4,575 exoplanets discovered made to date) during its nearly ten years of service. After examining the data collected by Kepler using a new deep-learning neural network called ExoMiner, a research team at NASA’s Ames Research Center was able to detect 301 more planetary signals and add them to the growing census of exoplanets.

July 8, 2021July 8, 2021 by Nancy Atkinson

Researchers Have Taught a Drone to Recognize and Hunt Down Meteorites Autonomously

Almahata Sitta 15. The black fragment of Almahata Sitta meteorite number 15 shows up black against the lighter coloured rocks of the Nubian desert in Northern Sudan. Image credit: Peter Jenniskens (SETI Institute/NASA Ames)

Planetary scientists estimate that each year, about 500 meteorites survive the fiery trip through Earth’s atmosphere and fall to our planet’s surface. Most are quite small, and less than 2% of them are ever recovered. While the majority of rocks from space may not be recoverable due to ending up in oceans or remote, inaccessible areas, other meteorite falls are just not witnessed or known about.

But new technology has upped the number known falls in recent years. Doppler radar has detected meteorite falls, as well as all-sky camera networks specifically on the lookout for meteors. Additionally, increased use of dashcams and security cameras have allowed for more serendipitous sightings and data on fireballs and potential meteorite falls.

A team of researchers is now taking advantage of additional technology advances by testing out drones and machine learning for automated searches for small meteorites. The drones are programmed to fly a grid search pattern in a projected ‘strewn field’ for a recent meteorite fall, taking systematic pictures of the ground over a large survey area. Artificial intelligence is then used to search through the pictures to identify potential meteorites.

October 14, 2020October 14, 2020 by Matt Williams

The Most Comprehensive 3D Map of Galaxies Has Been Released

Credit: Danny Farrow, Pan-STARRS1 Science Consortium and Max Planck Institute for Extraterrestrial Physics

Atop the summit of Haleakala on the Hawaiian island of Maui sits the Panoramic Survey Telescope and Rapid Response System, or Pan-STARRS1 (PS1). As part of the Haleakala Observatory overseen by the University of Hawaii, Pan-STARRS1 relies on a system of cameras, telescopes, and a computing facility to conduct an optical imaging survey of the sky, as well as astrometry and photometry of know objects.

In 2018, the University of Hawaii at Manoa’s Institute for Astronomy (IfA) released the PS1 3pi survey, the world’s largest digital sky survey that spanned three-quarters of the sky and encompassed 3 billion objects. And now, a team of astronomers from the IfA have used this data to create the Pan-STARRS1 Source Types and Redshifts with Machine Learning (PS1-STRM), the world’s largest three-dimensional astronomical catalog.

September 2, 2020September 2, 2020 by Matt Williams

Machine Learning Algorithm Scoops up 50 New Exoplanets

Advances in technology are having a profound impact on astronomy and astrophysics. At one end, we have advanced hardware like adaptive optics, coronographs, and spectrometers that allow for more light to be gathered from the cosmos. At the other end, we have improved software and machine learning algorithms that are allowing for the data to be analyzed and mined for valuable nuggets of information.

One area of research where this is proving to be invaluable is in the hunt for exoplanets and the search for life. At the University of Warwick, technicians recently developed an algorithm that was able to confirm the existence of 50 new exoplanets. When used to sort through archival data, this algorithm was able to sort through a sample of candidates and determine which were actual planets and which were false positives.

July 16, 2020July 16, 2020 by Evan Gough

A Stellar Stream of Stars, Stolen from Another Galaxy

The all-sky view that the Gaia survey would have of a simulated Milky-Way-like galaxy. [Credit: Sanderson et al. The Astrophysical Journal, January 6, 2020, DOI: 10.3847/1538-4365/ab5b9d]

Modern professional astronomers aren’t much like astronomers of old. They don’t spend every suitable evening with their eyes glued to a telescope’s eyepiece. You might be more likely to find them in front of a super-computer, working with AI and deep learning methods.

One group of researchers employed those methods to find a whole new collection of stars in the Milky Way; a group of stars which weren’t born here.