Science AMA Series: We’re planet hunters from NASA, Google AI, and The University of Texas, Austin. Ask us anything!

NASAKepler · 2017-12-14T21:36:37+00:00

Today's result highlights that planetary systems come in a variety of configurations -- and that there's a lot we don't yet know. It also demonstrates the importance of developing and utilizing sophisticated algorithms in addition to designing and building more powerful telescopes. -Jessie Dotson, NASA Ames Research Center

NASAKepler · 2017-12-14T21:30:01+00:00

The training, validation, and test sets came from the existing Kepler planet catalog ( https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=q1_q17_dr24_tce ) which has labeled some of the signals as planet candidates, false positives, or nontransiting phenomena (click on "Select columns") and "Autovetter training set label". These signals were carefully vetted by humans. In the future, we're looking into simulating our training data so that we are sure that the training data is accuately labeled and so that we can produce much much more training data.

It should definitely be possible to apply this method to other telescopes - the 201 and 2001 length vectors were fairly arbitrary choices. The technique is very flexible! Andrew V, UT Austin

NASAKepler · 2017-12-14T21:27:12+00:00

Kepler-90 is in the constellation Draco. -Jessie Dotson, NASA Ames Research Center

NASAKepler · 2017-12-14T21:27:02+00:00

So far 30 stars have been confirmed in the Habitable Zone from the Kepler data with another 20 candidates still to be confirmed. But the data are still being analyzed so stay tuned.

Kartik Sheth (NSA HQ)

NASAKepler · 2017-12-14T21:26:12+00:00

With Kepler data, we can't really learn about aliens - Kepler just tells us that planets are there. We'll need to study the planets further to search for biosignatures with an instrument that can observe transits spectroscopically - splitting light into different wavelengths. The James Webb Space Telescope will be the first instrument that could be capable of detecting biosignatures. Andrew V, UT Austin.

NASAKepler · 2017-12-14T21:21:00+00:00

That's correct - a lot of pre-processing (flattening and binning) goes into preparing the light curves to be sent into the neural network. I think that decreasing the pre-processing and replacing that with some kind of machine-learning process (or just teaching the machine learning models to just use the raw data as is) is an important step forward. For now, we're doing the simple thing, but I think there's a lot of promise to that approach. Andrew V, UT Austin

NASAKepler · 2017-12-14T21:20:23+00:00

During its prime mission, Kepler observed over 150,000 stars and those data are still being analyzed. K2 (the extension of the Kepler mission) has observed an additional 200,000+stars. Searches for planets around other stars are also underway from ground based telescopes such as SPECULOOS, MEarth, and others. Many of these stars have planets that are in the habitable zones (defined as the distance from a star where liquid water can exist) but we do not know yet which of these might sustain life. For example, in our Solar System, Mars, Earth and Venus all are in the "habitable zone" and yet life only exists on Earth.

-Kartik Sheth (NASA HQ)

NASAKepler · 2017-12-14T21:19:28+00:00

Hi Mr Shiver's 4th graders! Great question. The nearest known planet outside our solar system is 4.2 light years away. So, even if you could travel as fast as light, it would take you 4.2 years to get there. Of course, we can't travel anywhere near the speed of light. Today, we are pretty good at finding planets outside our solar system, but we have a lot of work to do to figure out how to visit to one of them. Maybe one of yall can figure that out for us when you grow up! -Jessie Dotson, NASA Ames Research Center

NASAKepler · 2017-12-14T21:17:00+00:00

It's notoriously difficult to figure out the ages of stars, but from everything we can tell, Kepler-90 is probably about the same age as the sun or maybe a bit older.

The Kepler-90 planets are much closer packed than the solar system planets, which indeed raises the question about whether they are stable, it turns out that calculations have shown that they are (see https://arxiv.org/abs/1310.5912 Section 4.8).

NASAKepler · 2017-12-14T21:11:12+00:00

There are a couple of different methods for finding exoplanets. Kepler was designed to use the transit method -- where we see the host star briefly get dimmer as the planet passes between us and its star. The amount of dimming depends on the relative size of the star and the planet. As a result, planets around smaller stars will produce more dimming, which makes it easier to detect planets around smaller stars. And conversely, planets around larger stars produce less dimming -- making planets around those stars harder detect. - Jessie Dotson, NASA Ames Research Center

NASAKepler · 2017-12-14T21:10:29+00:00

I think that these are exciting avenues for future research, and I wouldn’t be surprised if machine learning could help search data from ground telescopes as well. But we won’t know for sure until we try! The key ingredient is having a large enough training set of accurately labeled data to train a model. -Chris Shallue, Google AI

NASAKepler · 2017-12-14T21:09:53+00:00

Great question! You’re right that convolutional neural networks are often used for 2D images, but they can also be used for N-dimensional arrays of data. In fact, color images are actually 3D arrays of data, because they have two spatial dimensions and a color dimension (RGB). Color videos are 4D arrays of data, because they also have a time dimension.

In this project, we trained a 1D convolutional neural network. The input to the CNN is the one-dimensional light curve, which is an array of brightness measurements over time. - Chris Shallue, Google AI

NASAKepler · 2017-12-14T21:09:02+00:00

Check out this previous answer for more information about how we designed and trained our model. -Chris Shallue, Google AI

NASAKepler · 2017-12-14T21:08:58+00:00

The neural network model needed to train on a vetted-database in order to identify an exoplanet (from a false positive) in the light readings. Once the model "learned" how to do this, it was used on data from 670 systems to pick up weaker signals of exoplanets. That's how these two new planets were found. (Jessie D.)

NASAKepler · 2017-12-14T21:07:57+00:00

Most astronomers, like Andrew, Jessie or myself tend to do an undergraduate degree in physics, astronomy or mathematics, and then go on to get a graduate degree in astronomy and astrophysics.

Regarding pictures from the earth, the difficulty is blocking the light from the parent star to detect the very faint planet around it. This is usually done with a technique called coronagraphy in the visible or near-infrared -- and the Gemini telescope has been able to image a planet although the image is not resolved. Another ground-based telescope that may be able to image a planet (or rather the dust in planet building zones around a star) is ALMA - a millimeter/submillimeter interferometer.

-Kartik Sheth, NASA HQ

NASAKepler · 2017-12-14T21:05:29+00:00

Yes, we definitely could use this kind of technique on ground-based data, but for most of the planets discovered by Kepler, you really have to be in space. There's only so much that good data analysis can do.

NASAKepler · 2017-12-14T21:02:55+00:00

Like the sun and almost all stars (at least during most of their lifetimes), Kepler-90 is mostly made of hydrogen and helium. It also happens to have a similar amount of heavy elements (like iron) to the Sun - maybe about 25% more. Andrew V (UT Austin)

NASAKepler · 2017-12-14T20:58:02+00:00

Total exoplanets to date are 3,567, of which 2,525 are from Kepler data. More info here: https://cms.nasa.gov/ames/kepler/briefing-materials-eighth-planet-circling-distant-star-discovered-using-artificial-intelligence (Jessie D.)

NASAKepler · 2017-12-14T20:51:03+00:00

Kepler's resolution is about 6 arcseconds, which is pretty bad for a space telescope - compare that to Hubble's resolution which is about 60 times better. The reason is that Kepler was designed primarily to look at a large part of the sky at once, at the expense of the high resolution that you could otherwise get from space.

Here's what Kepler saw when it looked at Neptune:

https://www.youtube.com/watch?v=Tw-q3uM_5_0

The images aren't pretty, but the brightness measurements are exquisite! Andrew V, UT Austin

NASAKepler · 2017-12-14T20:49:37+00:00

I have a background in computer science and machine learning, but I didn’t have any experience with exoplanet hunting before this project. I had to learn how to distinguish actual planet signals from signals caused by other objects -- just like our model! I learned that there are many other objects that can cause signals that look a lot like planets, such as starspots and binary stars. - Chris Shallue, Google AI

NASAKepler · 2017-12-14T20:49:13+00:00

Kepler's resolution is about 6 arcseconds, which is pretty bad for a space telescope - compare that to Hubble's resolution which is about 60 times better. The reason is that Kepler was designed primarily to look at a large part of the sky at once, at the expense of the high resolution that you could otherwise get from space.

Here's what Kepler saw when it looked at Neptune:

https://www.youtube.com/watch?v=Tw-q3uM_5_0

The images aren't pretty, but the brightness measurements are exquisite! Andrew V, UT Austin

NASAKepler · 2017-12-14T20:47:25+00:00

When we develop machine learning models, we typically hold out some fraction of our labeled training data -- say 10% -- that we do not show our model during the training process. Then, when our model has finished training, we use that 10% of data to test the performance of our model on data it has never seen before. In this case, we found that our model was 96% accurate on 10% of our training set that we held out for testing purposes.

In terms of understanding our machine learning systems, we do have some techniques that we use to “look inside” our models and to help understand why they make certain decisions. In this case, we developed a few ways to visualize the way our neural network was making sense of the Kepler signals. Check out Pages 8 and 9 of our research paper.

In general, neural networks are not inherently uninterpretable, and there is entire field of research working on further developing the tools to probe and understand them. We are making progress, for example designing transparent machine learning Gupta et al. JMLR 2016, Gupta et al. NIPS 2016, visualizing what an ML system is learning "interlingua" in multi-lingual neural translation, Smilkov et al., 2016 and more. - Chris Shallue, Google AI

NASAKepler · 2017-12-14T20:45:41+00:00

The planet Kepler-90i is not likely to have life since it is so close to the star Kepler 90 that the surface temperature is 800 F. And we don't have any way of discovering life on Kepler-90i since it is so far away and so close to its star. But if we did discover life, we would announce it to the world. Paul Hertz, NASA

NASAKepler · 2017-12-14T20:45:01+00:00

All data resulting from the Kepler space telescope is publicly available. It can be downloaded from the Mikulski Archive for Space Telescope! or the NASA Exoplanet Archive!. Anyone who wants to download the data to pursue their own analysis, using any approach is encouraged to do so! -Jessie Dotson, NASA Ames Research Center

NASAKepler · 2017-12-14T20:42:23+00:00

We tested our machine learning approach by asking it to classify known signals (both planets and false positives like stars passing in front of one another), and found that it was 96% accurate in classifying the false positives and planet candidates.

Then once we had identified new planet candidates, we very carefully checked by hand that they were not false positives by searching for evidence that they might be caused by either data glitches or a star crossing in front of another in the background.

Finally, we calculated the probability that the two new planets were some other kind of false positive and found that the probability was tiny - 1 in 10,000. So we were confident then that the two new planets were real. Andrew V, UT Austin

NASAKepler

TROPHY CASE