Bringing the solar system to life in 3D with NASA

Bringing the solar system to life in 3D with NASA.

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3d imagery in google earth

The solar system has always been “teaching” us. You can see it in the drawings of the constellations in the Lascaux Caves. And the astronomy of the ancient world all the way up to now. When NASA is helping people around the world to get a closer look at the universe through explorations like the Artemis moon mission or the new James Webb Space images. Starting today. You can learn even more from the solar system with new experiences on Google Search and Google Arts & Culture. Thanks to NASA.

NASA and Google Arts & Culture have partnered to bring more than 60 3D models of planets. Moons and NASA spacecraft to Google Search. When you use Google Search to learn about these topics. Just click on the View in 3D button to understand the different elements of what you’re looking at even better. These 3D annotations will also be available for cells. Biological concepts (like skeletal systems). And other educational models on Search.

Bringing the solar system to life in 3D with NASA.

  • An image of the moon with a map of where Apollo 11 landedSee the Moon and check the Apollo 11 landing site.
Bringing the solar system
Bringing the solar system
Bringing the solar system

And, when you are on your mobile phone, you can even use Augmented Reality to project these models into your space.

Bringing the solar system
Bringing the solar system

And, when you are on your mobile phone, you can even use Augmented Reality to project these models into your space.

Bringing the solar system to life in 3D with NASA

See the Earth and other planets in your space.

You can also dive deeper into these celestial bodies. And learn about the scientific instruments NASA uses to study them. A new Google Arts & Culture project lets you explore the solar system. With stories about diamond rain on Neptune. Gas giants and dwarf planets. Saturn’s moons. And NASA missions like the Parker Solar Probe or Landsat. NASA will even take you on a tour of the Hubble Telescope’s greatest images. Show you what makes the International Space Station so extraordinary. And help you discover how the Mars Perseverance Rover is revealing our place in the universe.

  • an image of the planets with the text “Our solar system”Do you know which is the smallest planet in the Solar System and the closest to the Sun? Learn more here.

The experience is also a way of helping inspire future students who may be considering careers as our next generation of explorers. As Kris Brown, NASA’s Deputy Associate Administrator for STEM Engagement notes. “Our nation’s future in air and space starts with inspiring students. And igniting a spark of curiosity in STEM in today’s classrooms. The ability to see our solar system. And join missions like the James Webb Space Telescope in 3D will give students a unique perspective of space and, hopefully, engage them in expanding their understanding of our home planet and our place in the universe. We hope these students will begin a journey of learning on STEM pathways toward building our next generation of explorers.”

So whether you’re a student learning about the solar system. Or just want to get up close to the moon. We are thrilled to help NASA put the planets in the palm of your hand.

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And, when you are on your mobile phone, you can even use Augmented Reality to project these models into your space.

When NASA is helping people around the world to get a closer look at the universe through explorations like the Artemis moon mission or the new James Webb Space images. You can learn even more from the solar system with new experiences on Google Search and Google Arts & Culture. NASA and Google Arts & Culture have partnered to bring more than 60 3D models of planets. When you use Google Search to learn about these topics. These 3D annotations will also be available for cells.

When NASA is helping people around the world to get a closer look at the universe through explorations like the Artemis moon mission or the new James Webb Space images. You can learn even more from the solar system with new experiences on Google Search and Google Arts & Culture. NASA and Google Arts & Culture have partnered to bring more than 60 3D models of planets. When you use Google Search to learn about these topics. These 3D annotations will also be available for cells.

Earth to exoplanet: Hunting for planets with machine learning

Earth to exoplanet: Hunting for planets with machine learning

Earth to exoplanet

For thousands of years. People have looked up at the stars. Recorded observations. And noticed patterns. Some of the first objects early astronomers identified were planets, which the Greeks called. “Planētai.” Or “Wanderers.” for their seemingly irregular movement through the night sky. Centuries of study helped people understand that the Earth and other planets in our solar system orbit the sun a star like many others.

Earth to exoplanet: Hunting for planets with machine learning

Today, with the help of technologies like telescope optics, space flight, digital cameras. And computers. It’s possible for us to extend our understanding beyond our own sun and detect planets around other stars. Studying these planets called exoplanets helps us explore some of our deepest human inquiries about the universe. What else is out there?. Are there other planets and solar systems like our own?

Though technology has aided the hunt, finding exoplanets isn’t easy. Compared to their host stars. Exoplanets are cold, small. And dark about as tricky to spot as a firefly flying next to a searchlight … . From thousands of miles away. But with the help of machine learning, we’ve recently made some progress.

Earth to exoplanet: Hunting for planets with machine learning

One of the main ways astrophysicists search for exoplanets is by analyzing large amounts of data from NASA’s. Kepler mission with both automated software and manual analysis. Kepler observed about 200,000 stars for four years, taking a picture every 30 minutes. Creating about 14 billion data points. Those 14 billion data points translate to about 2 quadrillion possible planet orbits!. It’s a huge amount of information for even the most powerful computers to analyze. Creating a laborious, time-intensive process. To make this process faster and more effective. We turned to machine learning.

The measured brightness of a star decreases ever so slightly when an orbiting planet blocks some of the light. The Kepler space telescope observed the brightness of 200,000 stars for 4 years to hunt for these characteristic signals caused by transiting planets.

Machine learning is a way of teaching computers to recognize patterns. And it’s particularly useful in making sense of large amounts of data. The key idea is to let a computer learn by example instead of programming it with specific rules.

I’m a Google AI researcher with an interest in space.

And started this work as a 20 percent project (an opportunity at Google to work on something that interests you for 20 percent of your time). In the process, I reached out to Andrew, an astrophysicist from UT Austin, to collaborate. Together, we took this technique to the skies and taught a machine learning system how to identify planets around faraway stars.

Using a dataset of more than 15,000 labeled Kepler signals. We created a TensorFlow model to distinguish planets from non-planets. To do this. It had to recognize patterns caused by actual planets. Versus patterns caused by other objects like starspots and binary stars. When we tested our model on signals it had never seen before, it correctly identified which signals were planets and which signals were not planets 96 percent of the time. So we knew it worked!

Kepler 90i is the eighth planet discovered orbiting the Kepler 90 star, making it the first known 8-planet system outside of our own.

Armed with our working model. We shot for the stars. Using it to hunt for new planets in Kepler data. To narrow the search. We looked at the 670 stars that were already known to host two or more exoplanets. In doing so. We discovered two new planets: Kepler 80g. And Kepler 90i. Significantly, Kepler 90i is the eighth planet discovered orbiting the Kepler 90 star. Making it the first known 8-planet system outside of our own.

We used 15,000 labeled Kepler signals to train our machine learning model to identify planet signals. We used this model to hunt for new planets in data from 670 stars, and discovered two planets missed in previous searches.

Some fun facts about our newly discovered planet:

it’s 30 percent larger than Earth, and with a surface temperature of approximately 800°F—not ideal for your next vacation. It also orbits its star every 14 days, meaning you’d have a birthday there just about every two weeks.

Kepler 90 is the first known 8-planet system outside of our own. In this system, planets orbit closer to their star, and Kepler 90i orbits once every 14 days. (Note that planet sizes and distances from stars are not to scale.)

Earth to exoplanet: Hunting for planets with machine learning

The sky is the limit (so to speak) when it comes to the possibilities of this technology. So far, we’ve only used our model to search 670 stars out of 200,000. There may be many exoplanets still unfound in Kepler data, and new ideas and techniques like machine learning will help fuel celestial discoveries for many years to come.

To infinity, and beyond

Earthtopomaps.com

Though technology has aided the hunt, finding exoplanets isn’t easy. Compared to their host stars. But with the help of machine learning, we’ve recently made some progress. Kepler mission with both automated software and manual analysis. The measured brightness of a star decreases ever so slightly when an orbiting planet blocks some of the light.

Some fun facts about our newly discovered planet:

Earth to exoplanet: Hunting for planets with machine learning

Though technology has aided the hunt, finding exoplanets isn’t easy. Compared to their host stars. But with the help of machine learning, we’ve recently made some progress. Kepler mission with both automated software and manual analysis. The measured brightness of a star decreases ever so slightly when an orbiting planet blocks some of the light.

When we tested our model on signals it had never seen before, it correctly identified which signals were planets and which signals were not planets 96 percent of the time. So we knew it worked!

The sky is the limit (so to speak) when it comes to the possibilities of this technology. So far, we’ve only used our model to search 670 stars out of 200,000. There may be many exoplanets still unfound in Kepler data, and new ideas and techniques like machine learning will help fuel celestial discoveries for many years to come.

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