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NASA Alert: Strange Electric-Blue Clouds Detected Over Antarctica

January 12, 2018 Leave a comment

NASA has reported a mysterious electric-blue cloud formation over Antarctica they say may shed some light on the unusually cold weather being experienced across the east coast of America.

NASA’s AIM spacecraft has been deployed to monitor the mysterious cloud.

Wattsupwiththat.com reports: These are noctilucent clouds (NLCs), made of ice crystals frosting specks of “meteor smoke” in the mesosphere 83 km above the frozen continent. Here is an animation from the past week:

https://giphy.com/embed/3ohc1aQzGY7A3STjc4

This is the season for southern noctilucent clouds. Every year around this time, summertime water vapor billows up into the high atmosphere over Antarctica, providing moisture needed to form icy clouds at the edge of space.  Sunlight shining through the high clouds produces an electric-blue glow, which AIM can observe from Earth orbit.

“The current season began on Nov. 19th,” says Cora Randall, a member of the AIM science team at the University of Colorado’s Laboratory for Atmospheric and Space Physics. “Compared to previous years of AIM data, this season seems to be fairly average, but of course one never knows what surprises lie ahead, particularly since the southern hemisphere seasons are so variable.”

The formation of strange clouds in the high atmosphere over remote Antarctica may seem to be of little practical interest–but that would be incorrect.

Researchers studying NLCs have discovered unexpected teleconnections between these clouds and weather patterns thousands of miles away.

Two years ago, for instance, Randall and colleagues found that the winter air temperature in many northern US cities was well correlated with the frequency of noctilucent clouds over Antarctica. Understanding how these long-distance connections work could improve climate models and weather forecasting–all the more reason to study eerily beautiful NLCs.

NASA JPL trying to develop 2069 mission to Alpha Centauri

January 2, 2018 Leave a comment

NASA JPL is targeting an interstellar mission to launch by 2069 to Alpha Centauri.

A 2016 funding bill called upon NASA to investigate methods of interstellar travel that could reach at least 10 percent of the speed of light by 2069. It also requested a mission to Alpha Centauri, the closest star system to our own.

Scientists are working on the kinds of technology that would allow a probe to travel at 10 percent of the speed of light. Researchers are considering laser-powered probes, as well as nuclear propulsion, sail power and thrust derived from collisions between antimatter and matter.

Even at 10 percent of the speed of light, it would still take nearly a half-century to reach the star system, which lies 4.37 light years from our solar system. That leaves plenty of time for problems to arise.

Spacex Falcon Heavy is Vertical at the launch pad 39A at the Kennedy Space Center

December 30, 2017 Leave a comment

SpaceX’s first Falcon Heavy rocket is now vertical on launch pad 39A at NASA’s Kennedy Space Center. The fully-assembled 229-foot-tall rocket will be the most powerful in the world when it blasts off.

It will have 4.7 million pounds of thrust at launch. Elon has said first Falcon Heavy’s engines will only be operated at 92 percent of full power.

The Delta 4-Heavy rocket can take 63,471 pounds (28,790 kilograms) to low earth orbit.

SpaceX’s Falcon Heavy will be able to take 140,660 pounds (63,800 kilograms) to low Earth orbit.

NASA Releases Image Of Asteroid With Snowman Carved Onto Surface

December 26, 2017 Leave a comment

Nasa has released a stunning image of an asteroid with what appears to be a snowman carved onto its surface.

The weirdly festive image was captured by Nasa’s Dawn spacecraft in 2011. It features an asteroid with three adjacent impact craters on its surface.

Ibtimes.co.uk reports: Asteroids often collide with other objects as they hurtle through space, leaving their surfaces covered in various markings and craters.

“Do you want to build a snowman?” NASA tweeted alongside the photo. “Three well-placed impacts stacked this one on the surface of a giant asteroid.”

The mission, which was launched in 2007, is now exploring the dwarf planet Ceres – the largest object in the asteroid belt that lies between the orbits of Mars and Jupiter.

This is not the first time that Nasa has released intriguing images which appear to show recognisable objects on distant worlds.

In 2015, the agency’s Mars Reconnaissance Orbiter, which has been studying the Red Planet since 2006, captured an image of a region near the Martian pole which contained a feature resembling a smiley face.

In real life, the face measures around 500 metres across.

NASA Uses Robot To Uncover Eighth ‘Earth-Like’ Planet

December 19, 2017 Leave a comment

NASA has discovered a new Earth-like planet circling Kepler-90, a Sun-like star 2,545 light years from Earth, using new AI technology. 

The newly-discovered Kepler-90i was found using advanced machine learning from Google.

Spacedaily.com reports:  Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets.

“Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them,” said Paul Hertz, director of NASA’s Astrophysics Division in Washington. “This finding shows that our data will be a treasure trove available to innovative researchers for years to come.”

The discovery came about after researchers Christopher Shallue and Andrew Vanderburg trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler – the miniscule change in brightness captured when a planet passed in front of, or transited, a star.

Inspired by the way neurons connect in the human brain, this artificial “neural network” sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.

While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.

Other planetary systems probably hold more promise for life than Kepler-90. About 30 percent larger than Earth, Kepler-90i is so close to its star that its average surface temperature is believed to exceed 800 degrees Fahrenheit, on par with Mercury. Its outermost planet, Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.

“The Kepler-90 star system is like a mini version of our solar system. You have small planets inside and big planets outside, but everything is scrunched in much closer,” said Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.

Shallue, a senior software engineer with Google’s research team Google AI, came up with the idea to apply a neural network to Kepler data. He became interested in exoplanet discovery after learning that astronomy, like other branches of science, is rapidly being inundated with data as the technology for data collection from space advances.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Kepler’s four-year dataset consists of 35,000 possible planetary signals. Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data. However, the weakest signals often are missed using these methods. Shallue and Vanderburg thought there could be more interesting exoplanet discoveries faintly lurking in the data.

First, they trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue. In the test set, the neural network correctly identified true planets and false positives 96 percent of the time.

Then, with the neural network having “learned” to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets. Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.

“We got lots of false positives of planets, but also potentially more real planets,” said Vanderburg. “It’s like sifting through rocks to find jewels. If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well.”

Kepler-90i wasn’t the only jewel this neural network sifted out. In the Kepler-80 system, they found a sixth planet. This one, the Earth-sized Kepler-80g, and four of its neighboring planets form what is called a resonant chain – where planets are locked by their mutual gravity in a rhythmic orbital dance. The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.

Their research paper reporting these findings has been accepted for publication in The Astronomical Journal. Shallue and Vanderburg plan to apply their neural network to Kepler’s full set of more than 150,000 stars.

Kepler has produced an unprecedented data set for exoplanet hunting. After gazing at one patch of space for four years, the spacecraft now is operating on an extended mission and switches its field of view every 80 days.

“These results demonstrate the enduring value of Kepler’s mission,” said Jessie Dotson, Kepler’s project scientist at NASA’s Ames Research Center in California’s Silicon Valley.

“New ways of looking at the data – such as this early-stage research to apply machine learning algorithms – promises to continue to yield significant advances in our understanding of planetary systems around other stars. I’m sure there are more firsts in the data waiting for people to find them.”

Matrix Glitch: NASA Reveals Distorted Image Of Martian Surface

December 17, 2017 Leave a comment

Mac Slavo

An image released by NASA revealing a strange and distorted landscape on the surface of Mars is being dubbed a “matrix glitch.”  The odd image taken by the Mars Reconnaissance Orbiter shows intriguing clean breaks in the planet’s surface deposits.

The new view shows the different effects of fault activity on the Martian surface, giving rise to everything from clean breaks to ‘stretched out’ distortions.  This is likely an indication that the faults formed at different times when the layers were at various stages of hardening.

NASA explains the “glitchy” image in a second photo, using arrows.

In a second image offering a closer look at some of the features that look strange, NASA has pointed out where the faults have displaced individual beds. These areas, as noted by the yellow arrow, are where the faults produced a clean break. In other regions, as noted by the green arrow, the layers appear stretched out as they span the fault.

“These observations suggest that some of the faultings occurred while the layered deposits were still soft and could undergo deformation, whereas other faults formed later when the layers must have been solidified and produced a clean break,” NASA explained. 

Speckling the surface of one of Mars’ oldest impact basins, NASA’s Mars Reconnaissance Orbiter recently spotted a sprawling expanse of ‘honeycomb’ landforms, with individual cells of up to 6 miles wide.

The origin of these textured features has long remained a mystery, as scientists debate which type of natural process could be responsible, from glacial events to wind erosion. But it is still possible that multiple processes are at play, according to NASA. There is evidence suggesting the honeycombs and the surrounding landscape in Mars northwestern Hellas Planitia may still be undergoing activity today.

According to NASA, “the lack of impact craters suggest that the landscape, along with these features, have been recently reshaped by a process, or number of processes that may even be active today. Scientists have been debating how these honeycombed features are created, theorized from glacial events, lake formation, volcanic activity, and tectonic activity, to wind erosion.”

Artificial Intelligence helps NASA find 8th exoplanet in another solar system

December 16, 2017 Leave a comment

Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light-years from Earth. The planet was discovered in data from NASA’s Kepler Space Telescope.

The newly-discovered Kepler-90i – a sizzling hot, rocky planet that orbits its star once every 14.4 days – was found using machine learning from Google. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets.

“Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them,” said Paul Hertz, director of NASA’s Astrophysics Division in Washington. “This finding shows that our data will be a treasure trove available to innovative researchers for years to come.”

The discovery came about after researchers Christopher Shallue and Andrew Vanderburg trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler – the minuscule change in brightness captured when a planet passed in front of, or transited, a star. Inspired by the way neurons connect in the human brain, this artificial “neural network” sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.

While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.

Other planetary systems probably hold more promise for life than Kepler-90. About 30 percent larger than Earth, Kepler-90i is so close to its star that its average surface temperature is believed to exceed 800 degrees Fahrenheit, on par with Mercury. Its outermost planet, Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.

“The Kepler-90 star system is like a mini version of our solar system. You have small planets inside and big planets outside, but everything is scrunched in much closer,” said Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.

Shallue, a senior software engineer with Google’s research team Google AI, came up with the idea to apply a neural network to Kepler data. He became interested in exoplanet discovery after learning that astronomy, like other branches of science, is rapidly being inundated with data as the technology for data collection from space advances.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Kepler’s four-year dataset consists of 35,000 possible planetary signals. Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data. However, the weakest signals often are missed using these methods. Shallue and Vanderburg thought there could be more interesting exoplanet discoveries faintly lurking in the data.

First, they trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue. In the test set, the neural network correctly identified true planets and false positives 96 percent of the time. Then, with the neural network having “learned” to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets. Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.

“We got lots of false positives of planets, but also potentially more real planets,” said Vanderburg. “It’s like sifting through rocks to find jewels. If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well.”

Kepler-90i wasn’t the only jewel this neural network sifted out. In the Kepler-80 system, they found a sixth planet. This one, the Earth-sized Kepler-80g, and four of its neighboring planets form what is called a resonant chain – where planets are locked by their mutual gravity in a rhythmic orbital dance. The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.

Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and Eighth Planet Around Kepler 90

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