Showing posts with label space exploration. Show all posts
Showing posts with label space exploration. Show all posts
Wednesday, June 20, 2018
National-Near-Earth-Object-Preparedness-Strategy-and-Action-Plan
https://www.whitehouse.gov/wp-content/uploads/2018/06/National-Near-Earth-Object-Preparedness-Strategy-and-Action-Plan-23-pages-1MB.pdf
Friday, April 27, 2018
TU Delft Starship Team (DSTART)
https://youtu.be/ynv60pm6QdU
“In light of our insatiable appetite for exploration, it is inevitable that the human species will ultimately travel outside of the known solar system. It is the next step in human evolution.” These are the words of Angelo Vermeulen, doctoral candidate in the Systems Engineering section of the Faculty of TPM, and founder of the TU Delft Starship Team (DSTART). This is something of an unofficial dream team consisting of students and researchers collaborating on an evolving starship concept. Together with his team, Vermeulen creates computer simulations and algorithms, delving deeper into his doctoral research into adaptive systems for future space travel.
Holistic vision
DSTART is not developing hardware, nor is it building an actual spacecraft. Vermeulen is not involved with concrete product development; instead, he works on a holistic conceptual vision for extended periods of space exploration. Vermeulen: “My research concerns solutions that unite the biological, technological and social dimensions. And it is about spaceships that evolve during their journey.”
Multidisciplinary approach
The multidisciplinary approach is what makes Vermeulen’s doctoral research unique. His work blends elements of disciplines such as architecture, chemistry, biotechnology, astrophysics and computer simulation, but also art. Vermeulen himself can turn his hand to anything: he is an artist, a biologist and a space systems researcher. He can speak the languages of the various disciplines, which makes it easier for him to ‘break down walls’ and connect people. In this regard, he is also able to draw on his experience of co-creation and facilitating leadership during, for example, NASA’s HI-SEAS Mars simulation project and the Seeker art projects.
Creative chaos
In Seeker projects, people from various backgrounds collaborate to create an imaginative prototype of a spaceship. They actually reside in it for several days in order to experience first-hand what it is like to survive with each other. They are challenged to look beyond their respective fields of expertise and use their latent talents. It is a technological, ecological, social and artistic experiment in which the conscious decision is made to create without a predetermined plan. Vermeulen: “Creative chaos is required to find solutions for uncertainties that lay ahead. Systems need to develop themselves from the bottom up, which implicates that people need to be prepared to think for themselves. In this regard, significant gains are still to be made. A great deal of uncertainties is also evident in today’s society. Just think of the financial, political and climate crises that follow in rapid succession. My research is therefore also relevant to the pressing issues here on earth. Self-organisation, decentralised power and a strong participatory system play an essential role in finding solutions to these uncertain times.”
Seeker-art projects in Ljubljana (2013) and Enschede (2014). Pictures by Angelo Vermeulen, Willem-Jan Mengerink and IAA Architects.
Starship
A journey beyond our solar system could take decades, even a century. A starship therefore also needs to be capable of evolving. After all, returning to earth to tackle problems, as we saw during the subsequent missions towards the moon, is out of the question. The ship needs to be able to behave like a living organism that uses raw materials available in space, such as asteroids. These can be exploited for fuel and building materials, for example. “That is not just something for the future, companies are already investing in asteroid exploitation,” explains Vermeulen. 3D printers can be taken on board to print new parts and to expand the ship, as it also needs to accommodate a growing ecosystem. Human waste can be broken down by bacteria and converted into nourishment and CO2 for plants, which in turn, provide oxygen and food for the astronauts. ESA is currently developing such a self-supporting closed loop system in the MELiSSA project.
Dreams becoming reality
“It is precisely this inclusion of existing technology that makes our work less nebulous. It would be great if our contribution helps to bring interstellar travel another step closer.” Once he has completed his doctoral research, Vermeulen is in any case planning to remain active in the world of space travel. And the future looks bright. “Former NASA Director Pete Worden has already asked if I would be interested in working on Breakthrough Starshot, the first unmanned interstellar mission. For me, that would be a dream come true.”
“In light of our insatiable appetite for exploration, it is inevitable that the human species will ultimately travel outside of the known solar system. It is the next step in human evolution.” These are the words of Angelo Vermeulen, doctoral candidate in the Systems Engineering section of the Faculty of TPM, and founder of the TU Delft Starship Team (DSTART). This is something of an unofficial dream team consisting of students and researchers collaborating on an evolving starship concept. Together with his team, Vermeulen creates computer simulations and algorithms, delving deeper into his doctoral research into adaptive systems for future space travel.
Holistic vision
DSTART is not developing hardware, nor is it building an actual spacecraft. Vermeulen is not involved with concrete product development; instead, he works on a holistic conceptual vision for extended periods of space exploration. Vermeulen: “My research concerns solutions that unite the biological, technological and social dimensions. And it is about spaceships that evolve during their journey.”
Multidisciplinary approach
The multidisciplinary approach is what makes Vermeulen’s doctoral research unique. His work blends elements of disciplines such as architecture, chemistry, biotechnology, astrophysics and computer simulation, but also art. Vermeulen himself can turn his hand to anything: he is an artist, a biologist and a space systems researcher. He can speak the languages of the various disciplines, which makes it easier for him to ‘break down walls’ and connect people. In this regard, he is also able to draw on his experience of co-creation and facilitating leadership during, for example, NASA’s HI-SEAS Mars simulation project and the Seeker art projects.
Creative chaos
In Seeker projects, people from various backgrounds collaborate to create an imaginative prototype of a spaceship. They actually reside in it for several days in order to experience first-hand what it is like to survive with each other. They are challenged to look beyond their respective fields of expertise and use their latent talents. It is a technological, ecological, social and artistic experiment in which the conscious decision is made to create without a predetermined plan. Vermeulen: “Creative chaos is required to find solutions for uncertainties that lay ahead. Systems need to develop themselves from the bottom up, which implicates that people need to be prepared to think for themselves. In this regard, significant gains are still to be made. A great deal of uncertainties is also evident in today’s society. Just think of the financial, political and climate crises that follow in rapid succession. My research is therefore also relevant to the pressing issues here on earth. Self-organisation, decentralised power and a strong participatory system play an essential role in finding solutions to these uncertain times.”
Seeker-art projects in Ljubljana (2013) and Enschede (2014). Pictures by Angelo Vermeulen, Willem-Jan Mengerink and IAA Architects.
Starship
A journey beyond our solar system could take decades, even a century. A starship therefore also needs to be capable of evolving. After all, returning to earth to tackle problems, as we saw during the subsequent missions towards the moon, is out of the question. The ship needs to be able to behave like a living organism that uses raw materials available in space, such as asteroids. These can be exploited for fuel and building materials, for example. “That is not just something for the future, companies are already investing in asteroid exploitation,” explains Vermeulen. 3D printers can be taken on board to print new parts and to expand the ship, as it also needs to accommodate a growing ecosystem. Human waste can be broken down by bacteria and converted into nourishment and CO2 for plants, which in turn, provide oxygen and food for the astronauts. ESA is currently developing such a self-supporting closed loop system in the MELiSSA project.
Dreams becoming reality
“It is precisely this inclusion of existing technology that makes our work less nebulous. It would be great if our contribution helps to bring interstellar travel another step closer.” Once he has completed his doctoral research, Vermeulen is in any case planning to remain active in the world of space travel. And the future looks bright. “Former NASA Director Pete Worden has already asked if I would be interested in working on Breakthrough Starshot, the first unmanned interstellar mission. For me, that would be a dream come true.”
Tuesday, March 13, 2018
OS hunt for exo-planets
https://research.googleblog.com/2018/03/open-sourcing-hunt-for-exoplanets.html
Thursday, March 08, 2018
Posted by Chris Shallue, Senior Software Engineer, Google Brain Team
(Crossposted on the Google Open Source Blog)
Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research).
Today, we’re excited to release our code for processing the Kepler data, training our neural network model, and making predictions about new candidate signals. We hope this release will prove a useful starting point for developing similar models for other NASA missions, like K2 (Kepler’s second mission) and the upcoming Transiting Exoplanet Survey Satellite mission. As well as announcing the release of our code, we’d also like take this opportunity to dig a bit deeper into how our model works.
A Planet Hunting Primer
First, let’s consider how data collected by the Kepler telescope is used to detect the presence of a planet. The plot below is called a light curve, and it shows the brightness of the star (as measured by Kepler’s photometer) over time. When a planet passes in front of the star, it temporarily blocks some of the light, which causes the measured brightness to decrease and then increase again shortly thereafter, causing a “U-shaped” dip in the light curve.
A light curve from the Kepler space telescope with a “U-shaped” dip that indicates a transiting exoplanet.
However, other astronomical and instrumental phenomena can also cause the measured brightness of a star to decrease, including binary star systems, starspots, cosmic ray hits on Kepler’s photometer, and instrumental noise.
The first light curve has a “V-shaped” pattern that tells us that a very large object (i.e. another star) passed in front of the star that Kepler was observing. The second light curve contains two places where the brightness decreases, which indicates a binary system with one bright and one dim star: the larger dip is caused by the dimmer star passing in front of the brighter star, and vice versa. The third light curve is one example of the many other non-planet signals where the measured brightness of a star appears to decrease.
To search for planets in Kepler data, scientists use automated software (e.g. the Kepler data processing pipeline) to detect signals that might be caused by planets, and then manually follow up to decide whether each signal is a planet or a false positive. To avoid being overwhelmed with more signals than they can manage, the scientists apply a cutoff to the automated detections: those with signal-to-noise ratios above a fixed threshold are deemed worthy of follow-up analysis, while all detections below the threshold are discarded. Even with this cutoff, the number of detections is still formidable: to date, over 30,000 detected Kepler signals have been manually examined, and about 2,500 of those have been validated as actual planets!
Perhaps you’re wondering: does the signal-to-noise cutoff cause some real planet signals to be missed? The answer is, yes! However, if astronomers need to manually follow up on every detection, it’s not really worthwhile to lower the threshold, because as the threshold decreases the rate of false positive detections increases rapidly and actual planet detections become increasingly rare. However, there’s a tantalizing incentive: it’s possible that some potentially habitable planets like Earth, which are relatively small and orbit around relatively dim stars, might be hiding just below the traditional detection threshold — there might be hidden gems still undiscovered in the Kepler data!
A Machine Learning Approach
The Google Brain team applies machine learning to a diverse variety of data, from human genomes to sketches to formal mathematical logic. Considering the massive amount of data collected by the Kepler telescope, we wondered what we might find if we used machine learning to analyze some of the previously unexplored Kepler data. To find out, we teamed up with Andrew Vanderburg at UT Austin and developed a neural network to help search the low signal-to-noise detections for planets.
We trained a convolutional neural network (CNN) to predict the probability that a given Kepler signal is caused by a planet. We chose a CNN because they have been very successful in other problems with spatial and/or temporal structure, like audio generation and image classification.
Luckily, we had 30,000 Kepler signals that had already been manually examined and classified by humans. We used a subset of around 15,000 of these signals, of which around 3,500 were verified planets or strong planet candidates, to train our neural network to distinguish planets from false positives. The inputs to our network are two separate views of the same light curve: a wide view that allows the model to examine signals elsewhere on the light curve (e.g., a secondary signal caused by a binary star), and a zoomed-in view that enables the model to closely examine the shape of the detected signal (e.g., to distinguish “U-shaped” signals from “V-shaped” signals).
Once we had trained our model, we investigated the features it learned about light curves to see if they matched with our expectations. One technique we used (originally suggested in this paper) was to systematically occlude small regions of the input light curves to see whether the model’s output changed. Regions that are particularly important to the model’s decision will change the output prediction if they are occluded, but occluding unimportant regions will not have a significant effect. Below is a light curve from a binary star that our model correctly predicts is not a planet. The points highlighted in green are the points that most change the model’s output prediction when occluded, and they correspond exactly to the secondary “dip” indicative of a binary system. When those points are occluded, the model’s output prediction changes from ~0% probability of being a planet to ~40% probability of being a planet. So, those points are part of the reason the model rejects this light curve, but the model uses other evidence as well - for example, zooming in on the centred primary dip shows that it's actually “V-shaped”, which is also indicative of a binary system.
Searching for New Planets
Once we were confident with our model’s predictions, we tested its effectiveness by searching for new planets in a small set 670 stars. We chose these stars because they were already known to have multiple orbiting planets, and we believed that some of these stars might host additional planets that had not yet been detected. Importantly, we allowed our search to include signals that were below the signal-to-noise threshold that astronomers had previously considered. As expected, our neural network rejected most of these signals as spurious detections, but a handful of promising candidates rose to the top, including our two newly discovered planets: Kepler-90 i and Kepler-80 g.
Find your own Planet(s)!
Let’s take a look at how the code released today can help (re-)discover the planet Kepler-90 i. The first step is to train a model by following the instructions on the code’s home page. It takes a while to download and process the data from the Kepler telescope, but once that’s done, it’s relatively fast to train a model and make predictions about new signals. One way to find new signals to show the model is to use an algorithm called Box Least Squares (BLS), which searches for periodic “box shaped” dips in brightness (see below). The BLS algorithm will detect “U-shaped” planet signals, “V-shaped” binary star signals and many other types of false positive signals to show the model. There are various freely available software implementations of the BLS algorithm, including VARTOOLS and LcTools. Alternatively, you can even look for candidate planet transits by eye, like the Planet Hunters.
A low signal-to-noise detection in the light curve of the Kepler 90 star detected by the BLS algorithm. The detection has period 14.44912 days, duration 2.70408 hours (0.11267 days) beginning 2.2 days after 12:00 on 1/1/2009 (the year the Kepler telescope launched).
To run this detected signal though our trained model, we simply execute the following command:
python predict.py --kepler_id=11442793 --period=14.44912 --t0=2.2
--duration=0.11267 --kepler_data_dir=$HOME/astronet/kepler
--output_image_file=$HOME/astronet/kepler-90i.png
--model_dir=$HOME/astronet/model The output of the command is prediction = 0.94, which means the model is 94% certain that this signal is a real planet. Of course, this is only a small step in the overall process of discovering and validating an exoplanet: the model’s prediction is not proof one way or the other. The process of validating this signal as a real exoplanet requires significant follow-up work by an expert astronomer — see Sections 6.3 and 6.4 of our paper for the full details. In this particular case, our follow-up analysis validated this signal as a bona fide exoplanet, and it’s now called Kepler-90 i!
Our work here is far from done. We’ve only searched 670 stars out of 200,000 observed by Kepler — who knows what we might find when we turn our technique to the entire dataset. Before we do that, though, we have a few improvements we want to make to our model. As we discussed in our paper, our model is not yet as good at rejecting binary stars and instrumental false positives as some more mature computer heuristics. We’re hard at work improving our model, and now that it’s open sourced, we hope others will do the same!
If you’d like to learn more, Chris is featured on the latest episode of This Week In Machine Learning & AI discussing his work.
Thursday, March 08, 2018
Posted by Chris Shallue, Senior Software Engineer, Google Brain Team
(Crossposted on the Google Open Source Blog)
Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research).
Today, we’re excited to release our code for processing the Kepler data, training our neural network model, and making predictions about new candidate signals. We hope this release will prove a useful starting point for developing similar models for other NASA missions, like K2 (Kepler’s second mission) and the upcoming Transiting Exoplanet Survey Satellite mission. As well as announcing the release of our code, we’d also like take this opportunity to dig a bit deeper into how our model works.
A Planet Hunting Primer
First, let’s consider how data collected by the Kepler telescope is used to detect the presence of a planet. The plot below is called a light curve, and it shows the brightness of the star (as measured by Kepler’s photometer) over time. When a planet passes in front of the star, it temporarily blocks some of the light, which causes the measured brightness to decrease and then increase again shortly thereafter, causing a “U-shaped” dip in the light curve.
A light curve from the Kepler space telescope with a “U-shaped” dip that indicates a transiting exoplanet.
However, other astronomical and instrumental phenomena can also cause the measured brightness of a star to decrease, including binary star systems, starspots, cosmic ray hits on Kepler’s photometer, and instrumental noise.
The first light curve has a “V-shaped” pattern that tells us that a very large object (i.e. another star) passed in front of the star that Kepler was observing. The second light curve contains two places where the brightness decreases, which indicates a binary system with one bright and one dim star: the larger dip is caused by the dimmer star passing in front of the brighter star, and vice versa. The third light curve is one example of the many other non-planet signals where the measured brightness of a star appears to decrease.
To search for planets in Kepler data, scientists use automated software (e.g. the Kepler data processing pipeline) to detect signals that might be caused by planets, and then manually follow up to decide whether each signal is a planet or a false positive. To avoid being overwhelmed with more signals than they can manage, the scientists apply a cutoff to the automated detections: those with signal-to-noise ratios above a fixed threshold are deemed worthy of follow-up analysis, while all detections below the threshold are discarded. Even with this cutoff, the number of detections is still formidable: to date, over 30,000 detected Kepler signals have been manually examined, and about 2,500 of those have been validated as actual planets!
Perhaps you’re wondering: does the signal-to-noise cutoff cause some real planet signals to be missed? The answer is, yes! However, if astronomers need to manually follow up on every detection, it’s not really worthwhile to lower the threshold, because as the threshold decreases the rate of false positive detections increases rapidly and actual planet detections become increasingly rare. However, there’s a tantalizing incentive: it’s possible that some potentially habitable planets like Earth, which are relatively small and orbit around relatively dim stars, might be hiding just below the traditional detection threshold — there might be hidden gems still undiscovered in the Kepler data!
A Machine Learning Approach
The Google Brain team applies machine learning to a diverse variety of data, from human genomes to sketches to formal mathematical logic. Considering the massive amount of data collected by the Kepler telescope, we wondered what we might find if we used machine learning to analyze some of the previously unexplored Kepler data. To find out, we teamed up with Andrew Vanderburg at UT Austin and developed a neural network to help search the low signal-to-noise detections for planets.
We trained a convolutional neural network (CNN) to predict the probability that a given Kepler signal is caused by a planet. We chose a CNN because they have been very successful in other problems with spatial and/or temporal structure, like audio generation and image classification.
Luckily, we had 30,000 Kepler signals that had already been manually examined and classified by humans. We used a subset of around 15,000 of these signals, of which around 3,500 were verified planets or strong planet candidates, to train our neural network to distinguish planets from false positives. The inputs to our network are two separate views of the same light curve: a wide view that allows the model to examine signals elsewhere on the light curve (e.g., a secondary signal caused by a binary star), and a zoomed-in view that enables the model to closely examine the shape of the detected signal (e.g., to distinguish “U-shaped” signals from “V-shaped” signals).
Once we had trained our model, we investigated the features it learned about light curves to see if they matched with our expectations. One technique we used (originally suggested in this paper) was to systematically occlude small regions of the input light curves to see whether the model’s output changed. Regions that are particularly important to the model’s decision will change the output prediction if they are occluded, but occluding unimportant regions will not have a significant effect. Below is a light curve from a binary star that our model correctly predicts is not a planet. The points highlighted in green are the points that most change the model’s output prediction when occluded, and they correspond exactly to the secondary “dip” indicative of a binary system. When those points are occluded, the model’s output prediction changes from ~0% probability of being a planet to ~40% probability of being a planet. So, those points are part of the reason the model rejects this light curve, but the model uses other evidence as well - for example, zooming in on the centred primary dip shows that it's actually “V-shaped”, which is also indicative of a binary system.
Searching for New Planets
Once we were confident with our model’s predictions, we tested its effectiveness by searching for new planets in a small set 670 stars. We chose these stars because they were already known to have multiple orbiting planets, and we believed that some of these stars might host additional planets that had not yet been detected. Importantly, we allowed our search to include signals that were below the signal-to-noise threshold that astronomers had previously considered. As expected, our neural network rejected most of these signals as spurious detections, but a handful of promising candidates rose to the top, including our two newly discovered planets: Kepler-90 i and Kepler-80 g.
Find your own Planet(s)!
Let’s take a look at how the code released today can help (re-)discover the planet Kepler-90 i. The first step is to train a model by following the instructions on the code’s home page. It takes a while to download and process the data from the Kepler telescope, but once that’s done, it’s relatively fast to train a model and make predictions about new signals. One way to find new signals to show the model is to use an algorithm called Box Least Squares (BLS), which searches for periodic “box shaped” dips in brightness (see below). The BLS algorithm will detect “U-shaped” planet signals, “V-shaped” binary star signals and many other types of false positive signals to show the model. There are various freely available software implementations of the BLS algorithm, including VARTOOLS and LcTools. Alternatively, you can even look for candidate planet transits by eye, like the Planet Hunters.
A low signal-to-noise detection in the light curve of the Kepler 90 star detected by the BLS algorithm. The detection has period 14.44912 days, duration 2.70408 hours (0.11267 days) beginning 2.2 days after 12:00 on 1/1/2009 (the year the Kepler telescope launched).
To run this detected signal though our trained model, we simply execute the following command:
python predict.py --kepler_id=11442793 --period=14.44912 --t0=2.2
--duration=0.11267 --kepler_data_dir=$HOME/astronet/kepler
--output_image_file=$HOME/astronet/kepler-90i.png
--model_dir=$HOME/astronet/model The output of the command is prediction = 0.94, which means the model is 94% certain that this signal is a real planet. Of course, this is only a small step in the overall process of discovering and validating an exoplanet: the model’s prediction is not proof one way or the other. The process of validating this signal as a real exoplanet requires significant follow-up work by an expert astronomer — see Sections 6.3 and 6.4 of our paper for the full details. In this particular case, our follow-up analysis validated this signal as a bona fide exoplanet, and it’s now called Kepler-90 i!
Our work here is far from done. We’ve only searched 670 stars out of 200,000 observed by Kepler — who knows what we might find when we turn our technique to the entire dataset. Before we do that, though, we have a few improvements we want to make to our model. As we discussed in our paper, our model is not yet as good at rejecting binary stars and instrumental false positives as some more mature computer heuristics. We’re hard at work improving our model, and now that it’s open sourced, we hope others will do the same!
If you’d like to learn more, Chris is featured on the latest episode of This Week In Machine Learning & AI discussing his work.
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