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.

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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!

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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)

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.

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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
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.

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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.  

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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.

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Machine Learning Algorithm Scoops up 50 New Exoplanets

Credit: NASA/JPL-Caltech

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.

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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.

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Now that Many Countries Have the Ability to Destroy Satellites, the US is Figuring Out Ways to Make Them More Armored

An air-to-air left side view of an F-15 Eagle aircraft releasing an anti-satellite (ASAT) missile during a test. Credit: USAF

As long as human beings have been sending satellites into space, they have been contemplating ways to destroy them. In recent years, the technology behind anti-satellite (ASAT) weapons has progressed considerably. What’s more, the ability to launch and destroy them extends beyond the two traditional superpowers (the US and Russia) to include newcomers like India, China, and others.

For this reason, Sandia National Laboratories – a federal research center headquartered in New Mexico – has launched a seven-year campaign to develop autonomous satellite protection systems. Known as the Science and Technology Advancing Resilience for Contested Space (STARCS), this campaign will fund the creation of hardware and software that will allow satellites to defend themselves.

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AI Could Help the Europa Clipper Mission Make New Discoveries!

Artist's concept of a Europa Clipper mission. Credit: NASA/JPL

In 2023, NASA plans to launch the Europa Clipper mission, a robotic explorer that will study Jupiter’s enigmatic moon Europa. The purpose of this mission is to explore Europa’s ice shell and interior to learn more about the moon’s composition, geology, and interactions between the surface and subsurface. Most of all, the purpose of this mission is to shed light on whether or not life could exist within Europa’s interior ocean.

This presents numerous challenges, many of which arise from the fact that the Europa Clipper will be very far from Earth when it conducts its science operations. To address this, a team of researchers from NASA’s Jet Propulsion Laboratory (JPL) and Arizona State University (ASU) designed a series of machine-learning algorithms that will allow the mission to explore Europa with a degree of autonom.

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