1 00:00:00,660 --> 00:00:09,149 Hello, everyone. Thank you for coming out on a nearly sunny afternoon to sit inside this lecture theatre for a very special electron. 2 00:00:09,150 --> 00:00:13,230 Chris FLINTOFF I'm a professor of astrophysics here at the department, 3 00:00:13,500 --> 00:00:22,409 and I'm one of the people organising what's become a wonderful series of waiting workshops sponsored by Philip Whetton, who's is here. 4 00:00:22,410 --> 00:00:29,430 And for him you support we're very grateful. And this workshop is on the topic of planning for surprises. 5 00:00:30,330 --> 00:00:38,489 So it's an attempt to bring astronomers from different fields in different places for parts of the universe, 6 00:00:38,490 --> 00:00:46,080 I suppose together to talk about how in this world where we have access to huge data sets, we can still be surprised. 7 00:00:46,080 --> 00:00:50,370 We can find some things that we're not yet looking for. 8 00:00:50,670 --> 00:00:58,170 And so today, those of us at the scientific conference had talks about distant galaxies, about cosmology, 9 00:00:58,170 --> 00:01:06,270 the science of studying the universe as a whole, nearby galaxies, galaxies where you can distinguish what shape they are. 10 00:01:06,540 --> 00:01:15,839 We had talks about radio astronomy and we had kicked off with talks about acceptance planets around other stars. 11 00:01:15,840 --> 00:01:19,320 And that's where we're going to spend most of the next hour, 12 00:01:19,800 --> 00:01:24,540 an hour speaker who's going to talk on the topic of how to find planets around other stars. 13 00:01:25,230 --> 00:01:33,210 And delighted is David Hogg, who joins us. Fresh off the plane from New York, David started his career at MIT. 14 00:01:34,260 --> 00:01:38,040 He was at Princeton at the Institute of Advanced Study, 15 00:01:38,370 --> 00:01:44,729 told it's important for my dad that I mentioned that I never spent the rest of his time in New York, 16 00:01:44,730 --> 00:01:49,980 though the website of New York University says that he spends most of each summer in Heidelberg. 17 00:01:50,250 --> 00:01:56,399 And most of it I'm sorry, some of it somewhere in Heidelberg at some of each week at something called the Flatiron Institute, 18 00:01:56,400 --> 00:02:02,010 which is a new place in New York that's got the astronomers together to think about problems of big data. 19 00:02:02,010 --> 00:02:05,970 So there really is no one better to talk to us about the challenges ahead. 20 00:02:06,840 --> 00:02:12,670 And I invite you to find out exactly how we do find planets around other stars and Uranus. 21 00:02:16,520 --> 00:02:20,480 Thank you, Chris. That was very nice. Good. 22 00:02:20,510 --> 00:02:21,980 Let's get into it, shall we? 23 00:02:22,850 --> 00:02:30,070 So I think you probably heard a lot about machine learning and you have to be living under a rock to not have heard about machine learning. 24 00:02:30,080 --> 00:02:34,010 But let me just give it a little let's say a few words about what machine learning is, 25 00:02:34,190 --> 00:02:38,510 and then I'm really going to criticise it, but we'll talk about anyway, we'll see where it goes. 26 00:02:38,720 --> 00:02:43,580 So say you want to find all the kittens in all the YouTube videos and this sounds like a joke, 27 00:02:43,580 --> 00:02:49,129 but actually one of the first really big successes of deep learning was a 28 00:02:49,130 --> 00:02:52,760 demonstration by Google that they can find all the kittens in all the YouTube videos. 29 00:02:52,880 --> 00:02:58,100 And the way they did it is they essentially they they built they took this thing, 30 00:02:58,100 --> 00:03:03,260 which is called Deep Network or whatever, but it's essentially an extremely flexible function. 31 00:03:04,010 --> 00:03:11,569 And they found of the parameters of an extremely flexible function that can take a video in is out input and return a boolean. 32 00:03:11,570 --> 00:03:15,750 Yes, there's kittens or no there aren't kittens and that is true. 33 00:03:15,860 --> 00:03:21,499 Now, if you think about how complex videos are and how different they are, that is no mean feat. 34 00:03:21,500 --> 00:03:24,620 It's remarkable that they did that, although you can wonder why. 35 00:03:26,600 --> 00:03:32,749 But then fundamentally the reason it was possible is that they owned YouTube and 36 00:03:32,750 --> 00:03:37,700 so they could use any enormous data set of videos both with and without kittens, 37 00:03:38,060 --> 00:03:40,940 and to train this highly flexible function. 38 00:03:41,330 --> 00:03:48,649 It was the enormity of the data that made this possible, and that has a lot of connections to things that Chris and I talk about. 39 00:03:48,650 --> 00:03:53,870 We talk a lot about open science and open data, and we work a lot on kind of making astronomy more open. 40 00:03:54,050 --> 00:03:58,220 And one of the problems with these kinds of things is it really depends on what data you own. 41 00:03:58,400 --> 00:04:04,610 And notice the big leaders in deep learning are companies that do not share their data. 42 00:04:04,970 --> 00:04:11,660 And so it's an interesting question. What does this kind of model or this kind of operation have to do with science? 43 00:04:11,660 --> 00:04:17,210 And I think in its basic form, in that basic form, I think I would say not that much. 44 00:04:17,570 --> 00:04:21,770 Now, some people at the workshop here at this week will disagree with me strongly. 45 00:04:21,770 --> 00:04:25,430 And so people at the workshop are working hard to exploit these methods for science. 46 00:04:25,640 --> 00:04:28,700 I'm going to give a bit of a vision about that at some point later in the talk. 47 00:04:29,810 --> 00:04:36,470 Who knows, by the way, who here has an undergraduate degree in something relatively technical? 48 00:04:40,250 --> 00:04:45,000 Okay. I am going to disappoint you. Good. 49 00:04:45,110 --> 00:04:49,790 So I am going to talk about planets around other stars. I'm going to call them exoplanets in the business. 50 00:04:49,790 --> 00:04:54,889 We almost all call them exoplanets. But whenever you hear X or Y, that's jargon, but whenever you hear it, 51 00:04:54,890 --> 00:05:01,430 just think planets around other stars and just if you don't know about them, they were first discovered in the nineties. 52 00:05:01,430 --> 00:05:06,620 You probably I mean, the only thing that's been in the newspaper as much as deep learning is exoplanets. 53 00:05:06,620 --> 00:05:09,710 So basically and I'm going to make some fun of that later. 54 00:05:09,960 --> 00:05:17,720 And the first was out in the nineties and there are now thousands known which, 55 00:05:18,020 --> 00:05:22,999 you know, many people in this room are very young, but that's a pretty rapid advance. 56 00:05:23,000 --> 00:05:28,040 It went from being a very niche thing that nobody thought would work in the early nineties. 57 00:05:28,040 --> 00:05:32,450 In fact, there are talks. I remember going to seminars saying there's no way we'll ever detect mind around another star. 58 00:05:33,620 --> 00:05:41,780 215 years afterwards, two thirds of the astronomical community was working on exoplanets for at least part of that time. 59 00:05:41,840 --> 00:05:48,530 It really, really changed our community. And the reason it changed our community is that it is completely new class of objects. 60 00:05:48,860 --> 00:05:50,990 For one, we're always excited about new technologies. 61 00:05:51,230 --> 00:05:56,959 Two amazingly rich observationally, and I'm only going to give you the tiny sliver of that, how rich it is. 62 00:05:56,960 --> 00:06:03,260 Observationally and three, we live on a planet which makes it seem really important. 63 00:06:08,410 --> 00:06:13,430 You know, most of the things we do really have no applications and this is not going to be about applications here. 64 00:06:13,450 --> 00:06:17,380 In fact, that that kitten slide was the last application slide we have. 65 00:06:19,330 --> 00:06:24,790 We now know there are billions of planets in our galaxy. We basically know there are more planets then stars now. 66 00:06:25,000 --> 00:06:31,510 I mean, it's a little debateable exactly what you would say for the populations, but we but correcting for the selection of X and so on, 67 00:06:31,510 --> 00:06:40,450 we now believe that there are more planets and stars and and there are some regimes of planets, some kinds of planets we are not yet sensitive to. 68 00:06:40,450 --> 00:06:44,080 So I think once we become sensitive to those, it will be many more planets and stars. 69 00:06:44,290 --> 00:06:48,250 Do you agree with that? Yeah. And you could object to that. 70 00:06:49,030 --> 00:06:53,950 Okay, good. How could we know about these planets? Now, before I get there, I'm going to just remind you. 71 00:06:53,980 --> 00:06:58,450 Just remind you, in case you've forgotten any astronomy you learn. 72 00:06:58,450 --> 00:07:05,470 First of all, we orbit the sun. We're on a rock. Actually, we're really on a hunk of metal, but it's metal and rock. 73 00:07:06,010 --> 00:07:09,940 And we orbit the sun and we orbit the sun once every year. 74 00:07:11,830 --> 00:07:17,280 Depending a little bit on your reference, friend. I won't tell you that. 75 00:07:17,290 --> 00:07:22,239 In fact, it doesn't. There's no truth to whether or not there is goes around the sun and the sun goes around the earth. 76 00:07:22,240 --> 00:07:26,470 But we'll come to that later. They will do that in question period, if you like. 77 00:07:26,820 --> 00:07:31,770 And the sun is millions of times larger than the earth. 78 00:07:31,780 --> 00:07:34,089 The earth is tiny relative to the sun. 79 00:07:34,090 --> 00:07:40,000 And that's going to be an important part of the story, because finding planets is hard wise, finding planets hard because planets are tiny. 80 00:07:41,740 --> 00:07:48,040 And so we think of the earth as big. But you can fly around the earth. You can't fly around the sun for more reasons than once. 81 00:07:49,030 --> 00:07:54,460 The sun is orbiting the Milky Way. The Milky Way contains billions of stars, tens of billions of stars. 82 00:07:54,950 --> 00:08:00,040 And it depends a little bit what your definition of star is and so on. But many billions of stars in the Milky Way, 83 00:08:00,310 --> 00:08:12,000 the solar system is about 5 billion years old and we know the Earth is 4.6 very accurately and the universe is 13, 14 billion years old. 84 00:08:12,010 --> 00:08:16,540 So it's interesting thing that the earth is a significant fraction of the age of the entire universe. 85 00:08:17,140 --> 00:08:21,370 Just things to remember. Keep in mind, they're just some context for thinking about what we're working on. 86 00:08:22,660 --> 00:08:33,310 Okay, good. So in our solar system, the rocky planets are on the inside and the gas giants are on the outside. 87 00:08:33,370 --> 00:08:39,670 And all of my early scientific life, we believe that must be the way it is, 88 00:08:41,380 --> 00:08:45,270 because after all, the solar system is that way and everything seems typical about our star. 89 00:08:45,280 --> 00:08:51,940 So probably they're all like that. It's that turns out to be totally wrong. The inner planets get their heat from the sun. 90 00:08:52,330 --> 00:08:57,730 We are heated by the sun, although there is residual heat in the inside of the earth, as Icelanders know. 91 00:08:59,410 --> 00:09:05,170 And. But the outer planets get some heat from the sun and some heat from their original gravitational collapse. 92 00:09:05,170 --> 00:09:11,979 Jupiter is still cooling down from its original class, and there appears to be a continuum between planets and stars. 93 00:09:11,980 --> 00:09:17,290 There's no real distinction between planets and stars in the sense that the lowest mass 94 00:09:17,290 --> 00:09:22,030 star like objects we can see look very much like the highest mass planets we see. 95 00:09:22,270 --> 00:09:29,470 Looks like there's just a continuum between planets and stars, which is strange and was very surprising to the community. 96 00:09:31,170 --> 00:09:35,940 Good. How do we find plants? There's three big ways and then many little ways. 97 00:09:36,240 --> 00:09:44,040 Actually, there's about to be four big ways. But right now there's three. The one that happened in 1995 is radial velocity measurements. 98 00:09:44,190 --> 00:09:50,520 And the idea is the planet. And this the star and the planet orbit a common centre of mass. 99 00:09:51,030 --> 00:09:59,160 And so when when the planet is accelerating around the star, the star is also accelerating around the planet. 100 00:09:59,340 --> 00:10:04,170 Now, the star's accelerations in the stars. Velocities are very tiny, but they're not zero. 101 00:10:04,800 --> 00:10:16,200 And so, for instance, the sun moves something like ten or 15 centimetres is second in response to the planets in the solar system. 102 00:10:16,210 --> 00:10:22,380 So if you can measure the sun really accurately and we can of course measure our own sun that accurately, but that's not so interesting. 103 00:10:23,400 --> 00:10:30,000 And then radial velocity is you can measure velocities very accurately in the world because of the Doppler shift that things 104 00:10:30,000 --> 00:10:33,900 are blue shift and the red shift and blue shift when they're coming towards the register and then going away from you. 105 00:10:34,140 --> 00:10:37,410 And it's possible to measure those red shifts and blue shifts very precisely. 106 00:10:37,680 --> 00:10:41,280 So that was actually the first method that found planets around other stars. And there have been. 107 00:10:41,700 --> 00:10:47,159 Oops. Good. And there have been hundreds of discoveries from radial velocity. 108 00:10:47,160 --> 00:10:52,110 And many more than hundreds, thousands of stars have been studied carefully with radial velocity measurements. 109 00:10:52,710 --> 00:11:04,260 The big game is transits. If we're very lucky and the planet is orbiting the star in such a way that it passes between us and the star, 110 00:11:04,500 --> 00:11:10,950 then it blots out a little bit of the light each time it passes in front of the start star and blots out a little bit of the light. 111 00:11:11,280 --> 00:11:16,650 And those periodic transit, those are called transits, those little eclipses, they're like mini eclipses. 112 00:11:16,890 --> 00:11:21,570 And those eclipses are periodic signals that are imprinted on top of the stars brightness. 113 00:11:21,750 --> 00:11:24,690 So you can measure the brightness of a star unbelievably accurately. 114 00:11:24,690 --> 00:11:34,709 You can see these little blip as the planet goes in front, and an earth like planet blots out 100 parts per million, 115 00:11:34,710 --> 00:11:39,480 meaning a part in ten of the four 1/10000 of the light of the sun. 116 00:11:39,480 --> 00:11:44,960 So if some other astronomer on another star is looking back at us and sees the Earth Transit, 117 00:11:44,970 --> 00:11:54,420 it would block out ten to the minus four of the light of the sun for 13 hours every 365.25 days. 118 00:11:55,020 --> 00:12:00,120 So if the other astronomers have found us, they were very persistent. 119 00:12:02,790 --> 00:12:08,490 But the crazy thing is that NASA's Kepler spacecraft has found thousands of planets this way. 120 00:12:08,730 --> 00:12:13,170 And there are other missions both on the ground and in space that have also found many. 121 00:12:13,620 --> 00:12:20,639 So this has been the most productive, even though it requires this amazing coincidence that the orbit of the planet lie in along our line of sight. 122 00:12:20,640 --> 00:12:26,790 So it's only a tiny fraction of planets we can see this way. It's still been very productive, and I'll try to give you a sense of why. 123 00:12:28,180 --> 00:12:32,260 Of course, what we really want to do is just see the damn planet. 124 00:12:33,430 --> 00:12:36,770 We don't want to see it indirectly through the wobble or the light it blocks. 125 00:12:36,790 --> 00:12:41,019 We just want to see it. And so this is one of the holy grails for astronomy. 126 00:12:41,020 --> 00:12:47,140 And there have been a couple dozen planets found just by being directly imaged. 127 00:12:47,200 --> 00:12:53,650 You can just see them. But unfortunately, all the ones we can just see right now are very young planets. 128 00:12:53,890 --> 00:12:58,060 Planets that are so young, they're still very hot like the nebula they formed in. 129 00:12:58,300 --> 00:13:02,320 So we're really seeing very special planets when we directly image. 130 00:13:02,620 --> 00:13:11,330 Now it is on the roadmap for NASA and ESA to directly image that much, much smaller and much more normal planet. 131 00:13:11,350 --> 00:13:13,809 So. So this dream is alive. 132 00:13:13,810 --> 00:13:21,820 And there's a lot of astronomers right now in the United States working on mission concepts for this, and there's many other methods for finding them. 133 00:13:21,820 --> 00:13:27,160 A bunch of plants have been found through gravitational lensing, which is an absolutely wonderful thing, which are not to talk about at all. 134 00:13:28,090 --> 00:13:34,540 There's some planets have been found. A bunch of planets have been found because young stars have accretion, disk or gas disks. 135 00:13:34,540 --> 00:13:39,399 The planets form in gas disks around the star like our own solar system. 136 00:13:39,400 --> 00:13:44,950 Why is it in a plane? Probably because it formed out of a disk of dust and gas. 137 00:13:45,190 --> 00:13:49,659 And you can see in some young stars those disks of dust and gas. 138 00:13:49,660 --> 00:13:56,530 And you can see they're perturbed by planets. There have been planets found by their dynamical perturbations on other planets. 139 00:13:57,460 --> 00:14:02,020 And that's actually becoming a more and more productive way of finding planets over time or finding new in more 140 00:14:02,020 --> 00:14:06,910 and more ways to find planets through dynamical perturbations that go beyond the radial velocity methods. 141 00:14:07,210 --> 00:14:10,450 There have been planets found by pulsar timing and pulsation timing. 142 00:14:10,870 --> 00:14:14,139 And the really big thing is astrometry. 143 00:14:14,140 --> 00:14:22,330 They the you may have heard that the ESA Gaia mission just released very, very detailed information on 1.7 billion stars. 144 00:14:25,150 --> 00:14:26,770 This is just an early data release. 145 00:14:26,770 --> 00:14:35,410 When they do their late data releases, they will detect planets through the astrometric wobble of the star, just like the radial velocity wobble. 146 00:14:35,620 --> 00:14:37,840 There's also a wobble on the plane of the sky, 147 00:14:38,110 --> 00:14:46,079 and it's believed that Gaia might find tens of thousands of planets and might be the most productive producer of planets. 148 00:14:46,080 --> 00:14:49,570 So we don't know yet. Actually, it's one of the things I'm working on. 149 00:14:50,830 --> 00:14:57,100 Gaia, by the way, the guy database is very exciting thing, a very big moment for open science, a very big moment for astronomy. 150 00:14:57,940 --> 00:15:02,769 If you're interested, if you're an astronomy buff, look up the news on the guy I mentioned. 151 00:15:02,770 --> 00:15:05,229 It's really been remarkable and it's all the data. 152 00:15:05,230 --> 00:15:10,330 I've only been out for seven weeks and it's already had a huge impact on what we believe about the Milky Way and about stars. 153 00:15:12,010 --> 00:15:15,580 Good. I said this. Water exoplanets, they're planets around stars. 154 00:15:15,790 --> 00:15:21,940 But from my perspective, they are exceedingly tiny signals in exceedingly boring data. 155 00:15:22,810 --> 00:15:25,299 And I'm going to try and say why that work? 156 00:15:25,300 --> 00:15:33,940 Why does the data we kind of want the data to be boring, it turns out, because the exciting discoveries are easier to find in the boring data. 157 00:15:35,350 --> 00:15:46,580 What aren't exoplanets? They're not of this world and they're not in this. 158 00:15:47,540 --> 00:15:50,670 These are all massive press releases. But check this. 159 00:15:51,260 --> 00:15:56,650 They're not too bad. That is not what exit plans are. 160 00:15:56,800 --> 00:16:06,640 I mean, maybe for somebody they are actually, it was I was I went to the I don't know if people anybody here went to the eclipse this past summer. 161 00:16:06,640 --> 00:16:10,480 I was in central Oregon and a campsite. And then after that, we went to the Oregon coast. 162 00:16:10,480 --> 00:16:16,750 And I was with one of my colleagues who's in computer science who works on exoplanet detection with me for standing on the beach. 163 00:16:16,750 --> 00:16:26,310 And there's this mossy cliff with water dripping down it and there's whales reaching out in the ocean and the sun is starting to set. 164 00:16:26,320 --> 00:16:33,120 And he said, we have a little bit of hubris to think we know what these things are that we're detecting in the data, 165 00:16:33,220 --> 00:16:38,410 just looking at the richness that we see on Earth. But that is not what we've discovered. 166 00:16:39,340 --> 00:16:43,600 Okay, good. So I'm going to talk about Kepler mission because I'm going to focus on transits. 167 00:16:43,810 --> 00:16:50,740 But almost everything I say about transits will apply very much to all the other methods we've applied. 168 00:16:51,160 --> 00:16:55,750 Everything I say here, we have also applied to direct detection and everything I say here. 169 00:16:55,750 --> 00:17:01,209 We've also applied what we are applying to radial velocity and everything I say here, we're going to apply to Astrometry. 170 00:17:01,210 --> 00:17:07,480 So all of the different methods have the same kind of issue, the same kind of issue when it comes to data analysis. 171 00:17:07,480 --> 00:17:10,990 And I'm an I'm a data analyst, by the way. I'm like a software person. 172 00:17:10,990 --> 00:17:14,080 My my group is a software group and a methods group. 173 00:17:14,320 --> 00:17:21,219 We do a lot of statistics and we do a lot of data analysis and we build a lot of code and we all do open source, 174 00:17:21,220 --> 00:17:26,140 everything's open source and everything that I I'll show a few results from it, but not much. 175 00:17:26,140 --> 00:17:32,890 But the results I show will be results where you could get clone it from GitHub and type go and you will get those results. 176 00:17:35,620 --> 00:17:39,370 Good. The Kepler mission was unbelievably simple. That's what I said about boring. 177 00:17:39,640 --> 00:17:46,000 It just stared at 150,000 stars and delivered a brightness measurement every 30 minutes. 178 00:17:46,750 --> 00:17:51,040 That's all it did. Okay. It did a few other things, but not much else. 179 00:17:51,520 --> 00:17:59,739 I'm looking at Deirdre over there because he's one of the people of the Kepler mission office, and it lasted for 4.1 years. 180 00:17:59,740 --> 00:18:03,370 In its main mission, it made something like 10 billion measurements. 181 00:18:03,370 --> 00:18:06,940 During that time, it found thousands of planets, as I mentioned. 182 00:18:07,120 --> 00:18:13,569 And it's been followed by the K2 mission, which was a repurposing of Kepler after it basically got damaged. 183 00:18:13,570 --> 00:18:17,320 And that's how it ended its 4.1 year mission. 184 00:18:17,320 --> 00:18:23,260 But it's lived on. And actually the interestingly, the commission has been even more productive with some damaged satellites, 185 00:18:23,470 --> 00:18:25,870 which is another thing that we think about a lot. 186 00:18:25,900 --> 00:18:33,790 The group that's gathered here for this week for the wedding workshop is one of the themes of it is kind of repurposing things, 187 00:18:33,790 --> 00:18:38,410 repurposing data and repurposing hardware to do new things. 188 00:18:38,890 --> 00:18:45,010 And so that's a beautiful repurpose. The K2 mission actually has some responsibility for it. 189 00:18:45,370 --> 00:18:52,510 And here's how. One of the reasons that Kepler worked so well is that it's an unbelievably simple object. 190 00:18:52,720 --> 00:18:57,490 It's a telescope. So now, you know, it's a laser. So this is a telescope here. 191 00:18:58,150 --> 00:19:04,330 And then it's got its solar panels over here and it's got the only moving parts it has. 192 00:19:04,570 --> 00:19:11,050 I think this what I'm about to say may be slightly false, but essentially the only moving parts it has is for reaction wheels. 193 00:19:11,500 --> 00:19:14,610 And those were the things that failed to end its mission. 194 00:19:14,650 --> 00:19:16,930 Two of them failed and its primary mission. 195 00:19:17,110 --> 00:19:24,189 But it's an extremely simple device and it's just a telescope, very simple telescope, going to a very simple focal plane, that focal plain and simple. 196 00:19:24,190 --> 00:19:32,919 But it was very expensive. But the mission overall, because it's so simple, was very cheap by by like space launch standards. 197 00:19:32,920 --> 00:19:37,810 This is not an expensive mission. And in its main mission, it just stared at this one part of the sky. 198 00:19:38,020 --> 00:19:43,959 Now, the professional astronomers in the room will have no idea where this is on the sky, but some people in the audience might. 199 00:19:43,960 --> 00:19:50,050 Now, I certainly doubt and this this plot is way out of date. 200 00:19:50,410 --> 00:19:53,410 Anybody who works on Kepler is shaking their head that I'm showing this plot. 201 00:19:53,410 --> 00:20:02,380 But this plot is from the paper that I was a co-author on. And these the blue and black points are essentially all detected planets. 202 00:20:02,590 --> 00:20:07,419 The difference between blue and black has a little bit to do with the certainty with which they've been designed as planets. 203 00:20:07,420 --> 00:20:15,969 But but the story that's emerging is basically everything blue and black here is a planet, and I'm showing it an orbital period in days. 204 00:20:15,970 --> 00:20:25,990 So one year is like here ish and I'm showing you an orbital radius here where one is an earth radius and there's an orange dot at 365.2, 205 00:20:25,990 --> 00:20:30,760 five and one because the orange dots are the planets in our solar system. 206 00:20:31,950 --> 00:20:37,920 Remember, there's eight planets Mercury, Venus, Earth, Mars, and they go to Jupiter. 207 00:20:38,940 --> 00:20:44,520 Right. And all of these and know notice so many interesting things here. 208 00:20:44,550 --> 00:20:47,550 Look at these planets and Kepler found thousands of planets. Look at this. 209 00:20:47,550 --> 00:20:51,150 There's Mercury, the fastest planet in our solar system. 210 00:20:51,390 --> 00:20:55,740 Notice that almost all the planets that Kepler detected are faster than mercury. 211 00:20:56,400 --> 00:21:02,100 And many planetary systems have multiple planets inside the radius of mercury. 212 00:21:02,250 --> 00:21:07,570 So it looks a little weird. Another thing, another thing you can notice here is here's Earth. 213 00:21:07,590 --> 00:21:10,830 The goal of Kepler was to find planets that are like Earth. 214 00:21:11,400 --> 00:21:15,030 And notice that. What are these lines? 215 00:21:15,040 --> 00:21:18,330 These lines are kind of hardness. These planets are easy. 216 00:21:18,480 --> 00:21:21,540 Well, these are really easy to find. These are a little harder. These are a little harder. 217 00:21:21,540 --> 00:21:28,290 These are a lot harder. And so the reason that the planet population is dropping as we go this way is because it gets harder and harder to see them. 218 00:21:28,530 --> 00:21:35,700 Why does it get harder? Because as you go down, the planets get smaller and you go out and get fewer eclipses in 4.1 years. 219 00:21:36,720 --> 00:21:41,430 Right. You see. So you get smaller eclipses and fewer eclipses as you go out. 220 00:21:41,430 --> 00:21:45,059 And that's why that that basically it's very hard to find planets around here and it's 221 00:21:45,060 --> 00:21:49,500 very hard to find planets in the vicinity of Earth and Venus and not even close to Mars. 222 00:21:50,630 --> 00:21:56,270 It's so good. 223 00:21:56,280 --> 00:22:00,480 That's just some context. I think we'll come back to that. I'll come back to these results at some point later. 224 00:22:02,550 --> 00:22:06,720 This is actually key to data, not Kepler data, but it makes the point that I wanted to make, 225 00:22:07,620 --> 00:22:14,760 which is that the the the I'm going to talk about braces in parts per million and I'm going to centre them on zero. 226 00:22:14,850 --> 00:22:20,160 So think of this the following way. Imagine I was measuring a star and it had perfectly constant brightness. 227 00:22:20,580 --> 00:22:27,510 It would be set at zero. So this is like fluctuations in the star away from its mean behaviour. 228 00:22:27,750 --> 00:22:32,340 Okay. So zero would be no fluctuations and then I'm going to write things in parts per million. 229 00:22:32,850 --> 00:22:37,320 Right. So so if you just take raw data off the K2, this is a K2. 230 00:22:38,100 --> 00:22:45,150 I like her, not a candidate like her. But if you just take raw data off of it, there's kind of many thousands of parts per million variation. 231 00:22:45,360 --> 00:22:49,379 But if you look carefully at this variation, it's highly structured and very structured. 232 00:22:49,380 --> 00:22:57,330 That's structured because the spacecraft has configuration issues and the spacecraft 233 00:22:57,330 --> 00:23:02,550 is pointing and temperature is changing and that's projecting on to the data. 234 00:23:02,730 --> 00:23:06,420 And so one of the things we do is we kind of get rid of that spacecraft motion. 235 00:23:06,420 --> 00:23:12,140 And here's the saying light curve. This is the same star where we've kind of modelled. 236 00:23:12,150 --> 00:23:15,750 This is subtracting our best fit model of what the spacecraft is doing. 237 00:23:16,020 --> 00:23:21,780 And you see now we're getting more like 100 parts per million. And remember, that's the level at which we're looking for our transits. 238 00:23:22,680 --> 00:23:26,340 So this is kind of just an illustration that there's data. 239 00:23:26,700 --> 00:23:31,200 You don't just pull the data off the telescope and find planets, and that's going to be part of the story. 240 00:23:31,920 --> 00:23:35,909 And then this shows like a periodic, you know, you might have to be an astronomer to see this. 241 00:23:35,910 --> 00:23:41,610 This might look just like a noise to you. But if you're an astronomer, you see these little dangling little spikes. 242 00:23:42,120 --> 00:23:45,719 There's a data gap here that one of them and one of those down going spikes. 243 00:23:45,720 --> 00:23:51,209 So if you take the data and you fold it on that period, you see a transit, a planetary transit. 244 00:23:51,210 --> 00:23:56,280 So that's a planet that we found in the K2 data. Good. 245 00:23:56,700 --> 00:23:59,850 What did we learn from Kepler? We learned a huge amount from Kepler. 246 00:24:00,120 --> 00:24:05,280 Here's just some highlights. These are very personal highlights. Different people working in the mission would give you very different highlights. 247 00:24:05,460 --> 00:24:11,040 And I have to say, I'm not in the mission. I'm an outsider. I write software and I use other people's data. 248 00:24:13,140 --> 00:24:16,560 One thing we learned, as I said, there are comparable numbers of planets of stars, 249 00:24:16,560 --> 00:24:19,800 maybe more, I think probably more is my current rate of the situation. 250 00:24:20,520 --> 00:24:25,259 Many stars have very different planetary systems from our own, including these very close packed planets, 251 00:24:25,260 --> 00:24:34,500 planets on very short orbits, but also planets of around twice earths radius are the most common planets by far we now know. 252 00:24:34,980 --> 00:24:38,250 And there's no planet of that size in our solar system. 253 00:24:38,610 --> 00:24:44,159 So they're like they're like the factors of a few or maybe even ten more probable than Earth's and Neptune. 254 00:24:44,160 --> 00:24:53,870 So we have an Earth and a Neptune, but we have nothing in between. And we and Jupiter, like planets, however, are very common. 255 00:24:53,880 --> 00:24:57,810 We're learning and it looks like a very large fraction of stars have a kind 256 00:24:57,810 --> 00:25:02,490 of outer gas giant and might be essentially all stars where the jury's out. 257 00:25:02,490 --> 00:25:06,750 But it's very basically every star we've looked really hard at for an outer Jupiter. 258 00:25:06,750 --> 00:25:09,920 It looks like they might have. Good. 259 00:25:10,030 --> 00:25:13,350 I said that. Okay, good. 260 00:25:13,360 --> 00:25:14,319 I mentioned this earlier. 261 00:25:14,320 --> 00:25:22,660 If you're trying to find Earth, you need to find something that does 100 parts per million for 13 hours every 365, 3 to 5 days. 262 00:25:22,930 --> 00:25:28,060 Now, the problem is spacecraft variability, as I just showed you, is bigger than that. 263 00:25:28,270 --> 00:25:37,090 And also for many of the stars we study, including the sun, just the natural brightness, variations of the sun are larger than that. 264 00:25:37,330 --> 00:25:39,980 Actually, it depends a little bit on what phase the sun is. You're the sun. 265 00:25:40,000 --> 00:25:44,649 Sun has a 26 year cycle and it has an active phase and then an inactive phase 266 00:25:44,650 --> 00:25:48,370 and it cycles back and forth between active and inactive on a 26 year period. 267 00:25:48,700 --> 00:25:55,450 Anyway, in its active phase, the sun varies by more than that, and in its inactive phase actually varies a little less than that. 268 00:25:55,780 --> 00:26:00,910 And actually one of the interesting things about this is it's easier to find planets around inactive stars, 269 00:26:01,330 --> 00:26:05,830 which is a long story that people are thinking that good. 270 00:26:05,920 --> 00:26:15,430 So is it impossible to find Earths? Given that we're trying to find them in the face of noise, that is larger an answer to it. 271 00:26:15,430 --> 00:26:20,650 And obviously the answer is no because we found lots of planets that are getting very, very close to earth. 272 00:26:21,970 --> 00:26:30,520 So let's talk about why. The reason it's possible for us to find planets in the face of these noise sources 273 00:26:30,520 --> 00:26:35,200 is exactly the reason it's possible for Google to find kittens in YouTube videos. 274 00:26:36,700 --> 00:26:40,419 It is because we have enormous numbers of stars. 275 00:26:40,420 --> 00:26:45,070 The first thing about Kepler was not that it stared for 4.1 years. 276 00:26:45,310 --> 00:26:50,380 The genius thing about Kepler was that it stared at 150,000 stars for 4.1 years. 277 00:26:50,530 --> 00:26:53,590 In fact, it would have been better if it had stared at a million stars. 278 00:26:55,270 --> 00:27:03,219 We would have done more and we would have done better because in fact, our inferences about the Kepler data are limited by the amount of data we have. 279 00:27:03,220 --> 00:27:05,830 You might think, well, 10 billion data points. Isn't that enough? 280 00:27:06,130 --> 00:27:14,590 Well, it's not because we'd like to train flexible models, like the models that can take a video and tell you with a candidate, 281 00:27:14,920 --> 00:27:18,549 we want to take models that can take a star's light curve. 282 00:27:18,550 --> 00:27:26,860 Its behaviour over time and predict its future may take its past behaviour and predict its future, take its future behaviour and predict its past. 283 00:27:27,130 --> 00:27:33,130 We want informative models or informative functions that can predict the behaviours of stars. 284 00:27:33,610 --> 00:27:38,139 So there's sort of two aspects to this big data thing. 285 00:27:38,140 --> 00:27:46,780 The first thing is we use the large amount of data to learn flexible models that can predict how a star varies. 286 00:27:47,230 --> 00:27:52,240 Stars vary statistically, but not unpredictably because they are convecting. 287 00:27:52,420 --> 00:27:55,629 It's a big ball of fire, it's got a convecting surface, 288 00:27:55,630 --> 00:28:02,410 and that surface has temperature and brightness variations that are stochastic, but they're not completely unpredictable. 289 00:28:02,800 --> 00:28:16,000 And similarly similar but kind of orthogonal to the spacecraft is very and remember I showed you there is that spiky behaviour by the spacecraft. 290 00:28:16,270 --> 00:28:22,479 How do we figure that out? Well, we figured that out because if two stars very together, if I look at one star in the Kepler field, 291 00:28:22,480 --> 00:28:27,969 in another star in the Kepler field in general, these stars are hundreds of thousands of light years apart. 292 00:28:27,970 --> 00:28:32,560 They're not near each other in any sense. So they are not going to move in a synchronised way. 293 00:28:32,560 --> 00:28:38,290 They're not going to do sinking. They're not going to synchronised swim across that whole field. 294 00:28:38,470 --> 00:28:45,040 So if stars vary in concert, we can use the covariance of the stars to learn about what the spacecraft is doing. 295 00:28:45,340 --> 00:28:50,440 But once again, you need an enormous number of stars because you have to see a lot of stars all move together to 296 00:28:50,440 --> 00:28:55,810 infer that that is coming from the spacecraft rather than from from of planet or from the stars. 297 00:28:56,290 --> 00:29:05,290 So the idea behind the data science that we do in Kepler is we take the data, we use it to build a flexible predictive model of how stars work. 298 00:29:05,500 --> 00:29:09,730 And we use it to move to build a predictive model of what the spacecraft does. 299 00:29:10,210 --> 00:29:13,360 And that's how we find the planets. 300 00:29:13,360 --> 00:29:19,929 Now, that isn't quite the whole story. So we built these models. 301 00:29:19,930 --> 00:29:23,979 What how can a star very predictive model have started? Very we built the model. 302 00:29:23,980 --> 00:29:30,970 How is the spacecraft carrying now? We need a model for how the planet transits. 303 00:29:31,240 --> 00:29:38,950 The nice thing is and the thing that's most important is our expectation about how a planet transits it is a very rigid expectation. 304 00:29:39,160 --> 00:29:44,980 We knew exactly what a transit should look like, have a very simple shape and they're periodic in nature. 305 00:29:45,610 --> 00:29:54,250 And so we are talking about stochastic models for the star in the spacecraft, and we're talking about a very rigid model for the planet. 306 00:29:54,520 --> 00:30:02,110 And it's that mix of having a having a crazy flexible models under the stars, but a very simple, 307 00:30:02,380 --> 00:30:08,290 rigid, beautiful model for the transit that lets us find the transits in the noisy data and that. 308 00:30:08,530 --> 00:30:15,429 Problem structure is very general, by the way. That problem structure is also one of the things that is motivating the workshop we're 309 00:30:15,430 --> 00:30:23,170 doing at Christ Church because the it's easy to find the signals you're looking for, 310 00:30:23,410 --> 00:30:28,450 it's hard to find the signals you're not looking for. And transits are signals we are looking for. 311 00:30:28,450 --> 00:30:31,870 So that's something we can talk about in the discussion. There's lots to say about that. 312 00:30:32,020 --> 00:30:32,920 It's very interesting. 313 00:30:34,180 --> 00:30:40,600 So it's really this contrast between the flexible models we use for the new sciences and the rigid model we use for the planets. 314 00:30:40,810 --> 00:30:49,300 That's what makes it possible for us to find the planets. And it is very related somehow to this question of where are the kittens in the two videos? 315 00:30:50,440 --> 00:30:54,310 By the way, many problems in natural science have this structure. 316 00:30:54,340 --> 00:31:01,180 This is very common. Like if you work on, say you're trying to measure neurones, you're trying if you're doing real time imaging, 317 00:31:01,180 --> 00:31:06,400 calcium fluorescence imaging or whatever of of a slice of the brain and there's neurones firing. 318 00:31:06,700 --> 00:31:12,790 There's an immense amount of nuisance information, which is all the kind of noise in the image and all of the shapes of the neurones and stuff. 319 00:31:12,790 --> 00:31:22,150 You're just trying to identify which neurone is, is, is, whatever they call it, spiking at certain times and you're trying. 320 00:31:22,360 --> 00:31:25,120 So there's a very simple thing which you're asking for certain very, 321 00:31:25,120 --> 00:31:29,590 very well defined spikes are trying to draw them out of the data, but the data contains tons of nuisances. 322 00:31:29,830 --> 00:31:32,560 So it's a very common structure for problems in science. 323 00:31:33,280 --> 00:31:39,070 And it is often the case, especially in physics, that the things we care about have a very simple form. 324 00:31:40,540 --> 00:31:47,919 And so so my view is that we can harness machine learning to and to help us solve these 325 00:31:47,920 --> 00:31:52,390 problems because the machine learning can handle the part of the problem we don't care about. 326 00:31:53,080 --> 00:31:59,820 And if you go back to the kittens, why did I say that? I didn't think the kittens finding the kittens YouTube videos didn't seem like science to me. 327 00:31:59,830 --> 00:32:04,780 Why not? Because we didn't learn anything. We didn't learn anything about either kittens or YouTube videos. 328 00:32:04,780 --> 00:32:08,020 From that experience, we just learned that Google's awesome. 329 00:32:10,330 --> 00:32:16,239 What we need is when we're science, when we're when we're when we're scientists, 330 00:32:16,240 --> 00:32:19,360 we're trying to understand where we want to come to some new understanding. 331 00:32:19,380 --> 00:32:27,550 We want to make new discoveries that fit into some other understanding. We understand things about the about planets by finding the planets. 332 00:32:27,730 --> 00:32:32,260 We don't care about the stellar variability. Actually, some people do care deeply about stellar variability. 333 00:32:32,830 --> 00:32:37,030 But in in this context, we don't care about stellar variability. 334 00:32:37,210 --> 00:32:42,220 And and so we can use a model that leads to no understanding. 335 00:32:42,220 --> 00:32:45,690 It just handles it. By the way. 336 00:32:45,690 --> 00:32:50,160 So as I said, I was going to disappoint the people with technical backgrounds in the room. 337 00:32:50,310 --> 00:32:53,430 I did put a set of words on this slide. 338 00:32:53,440 --> 00:32:57,450 So if you want to do Wikipedia searching of things that's relevant. 339 00:32:59,310 --> 00:33:05,100 There's a if you go sort of the way we kind of think about these machine learning things. 340 00:33:05,340 --> 00:33:14,190 Most of the work we do is actually with very, very old technology, which is pure linear models, and linear models have immense capacity. 341 00:33:14,190 --> 00:33:20,570 So if you're interested in going down the sort of how does machine learning work and what would be how do I understand machine learning? 342 00:33:20,580 --> 00:33:25,230 I would start with linear models because it's just incredible what general linear models can do. 343 00:33:25,440 --> 00:33:33,780 And then we go up to Gaussian processes, which are kind of a generalisation of linear models to very large function spaces. 344 00:33:33,780 --> 00:33:40,140 They're very flexible things, they're like deep learning, but they have kind of better scientific properties in some ways. 345 00:33:40,290 --> 00:33:42,900 And then of course, there's deep learning, which is the thing you've always seen. 346 00:33:42,990 --> 00:33:52,200 And in, in the Star case, the, the, the, the most interesting technology is called recurrent, which is about imposing a certain kind of symmetry. 347 00:33:52,530 --> 00:33:57,780 And then there's a new technique that's emerging in machine learning called generative adversarial networks, 348 00:33:58,020 --> 00:34:03,030 which is likely to have a big impact in these kinds of projects in the future, but early days. 349 00:34:03,450 --> 00:34:09,060 So if you're interested, there's some words to chase down. Okay, good. 350 00:34:10,560 --> 00:34:15,070 Everything I said about transits also applies to radial velocities. 351 00:34:15,840 --> 00:34:21,240 When people when we measure radial velocity is we're trying to measure the way of loss of a star too much better than one metre per second. 352 00:34:21,480 --> 00:34:25,709 The Holy Grail is ten centimetres per second because it ten centimetres per second. 353 00:34:25,710 --> 00:34:32,670 You might be able to find Earth. And a star is a big, nasty object. 354 00:34:32,670 --> 00:34:40,650 The surface of the star, so the surface of the sun, the little patches of the surface of the sun are moving at a kilometre a second. 355 00:34:41,640 --> 00:34:44,670 The typical kind of convection speed is a kilometre second. 356 00:34:44,670 --> 00:34:52,530 We're trying to measure the surface of the sun to a metre per second, so we have to average over the sun and then or make a predictive model. 357 00:34:52,530 --> 00:34:56,220 We have to average over the motions on the surface of the sun or make a predictive model. 358 00:34:56,490 --> 00:35:01,410 And so, of course what we're working on is trying to build a predictive model of stellar surface speeds. 359 00:35:01,590 --> 00:35:04,830 And then there's also some terrible things about the atmosphere that come in. 360 00:35:05,070 --> 00:35:08,940 The tiny details of the atmosphere have a big impact when you're trying to measure things. 361 00:35:09,060 --> 00:35:13,200 After all, the wind is a lot faster than one metre per second. 362 00:35:14,340 --> 00:35:19,530 So, you know, that says that there are going to be imprints on the spectrum that are at the right level or at the bad level. 363 00:35:20,640 --> 00:35:24,270 But once again, elliptical orbit signature is very rigid. 364 00:35:24,270 --> 00:35:29,159 So we're competing a very flexible model to understand the stellar surface and the atmosphere. 365 00:35:29,160 --> 00:35:33,120 And we officially don't care about the stellar surface. We officially don't care about the atmosphere. 366 00:35:33,300 --> 00:35:38,040 All we care about is finding the planet. So it has the same structure and we're exploring the same place. 367 00:35:38,640 --> 00:35:44,600 I mentioned Suzanne Abrams is right. There is one of the world's experts in thinking about those questions and. 368 00:35:47,950 --> 00:35:48,310 Good. 369 00:35:48,400 --> 00:35:56,830 I need to raise an epistemological point, and I want to end on this theological point, because I think it's something that's worthy of discussion. 370 00:35:58,120 --> 00:36:05,980 If you think about the most interesting planets that have come out from Kepler, many of them are the ones that are most similar to Earth, 371 00:36:06,370 --> 00:36:10,300 meaning the ones that are small and the ones that are on long periods and they're rocky. 372 00:36:10,420 --> 00:36:16,360 By the way, we know those small planets are rocky now and through very interesting measurements, 373 00:36:16,450 --> 00:36:27,040 which I do have to ask answer questions about, the planets that are most interesting are in many cases the ones that are hardest to find. 374 00:36:27,910 --> 00:36:37,210 And if you ask yourself, well, how are we sure that those really are planets and not something in the data that's fooling us? 375 00:36:38,290 --> 00:36:44,980 Well, what's the most reliable things you can do? The most reliable thing you can do is go observe it with a new mission. 376 00:36:46,270 --> 00:36:54,070 But then you'd have to launch another Kepler. And Kepler is about as sensitive as a telescope could ever be of its type. 377 00:36:54,310 --> 00:36:58,690 It's not obvious you could make a telescope that's ten times more sensitive than Kepler. 378 00:36:58,990 --> 00:37:09,190 So it's possible that these most interesting planets from Kepler will never be verified externally, which is a little strange. 379 00:37:10,130 --> 00:37:14,750 And and we are pretty confident because we are very common, 380 00:37:14,770 --> 00:37:19,900 because then we can externally validate the planets that are a bit more massive and a bit 381 00:37:20,200 --> 00:37:25,210 shorter period and a bit bigger than we can verify using radial velocity measurements. 382 00:37:25,450 --> 00:37:31,750 We're confident that we're not being fooled at the bottom end, but I have to say we don't have external validation. 383 00:37:31,930 --> 00:37:35,730 And for the scientists, external validation is by far the most important thing. 384 00:37:36,030 --> 00:37:39,009 And I want in general and as I put this slide back up, 385 00:37:39,010 --> 00:37:46,090 because I just wanted to remind you that many of these planets are extremely interesting and they're basically impossible to verify. 386 00:37:47,740 --> 00:37:55,120 There are some clever ways these blast points that are below this line have been verified through timing measurements. 387 00:37:55,120 --> 00:37:57,070 So there are some ways you can do it better. 388 00:37:57,250 --> 00:38:03,940 But in general, most of these things that are interesting here in near Earth are very, very hard or impossible to verify. 389 00:38:04,450 --> 00:38:07,690 So I wanted to just generalise that a little bit. 390 00:38:08,440 --> 00:38:15,460 It's a very interesting thing about astrophysics. The the epistemological status of astrophysics is very strange. 391 00:38:15,540 --> 00:38:19,390 And I'm in a physics department and I consider myself a physicist. 392 00:38:19,630 --> 00:38:25,240 But many physicists are made somewhat uncomfortable by astrophysics for a particular reason, 393 00:38:25,510 --> 00:38:30,760 which is that the objects of our study are incredibly remote and we don't get to manipulate them. 394 00:38:30,940 --> 00:38:34,899 You can't decide to have two neutron stars collide. 395 00:38:34,900 --> 00:38:40,840 You have to wait for it to happen and you have to hope you're looking in the right direction when it does happen. 396 00:38:41,890 --> 00:38:49,060 There's no chance of sample return and even the sun sample return would be almost an impossible mission. 397 00:38:49,060 --> 00:38:57,730 But it's a great idea. We should figure it out. We could only do radar inside our own solar system and even that out to the outer solar system. 398 00:38:58,100 --> 00:39:01,270 And we dependent very heavily on chance. 399 00:39:01,270 --> 00:39:10,329 What happens to hit the telescope aperture when the shutter happens to be open and almost everything comes to us through photons actually. 400 00:39:10,330 --> 00:39:14,950 Of course, an important thing over the last two years is that we now have gravitational waves and we have neutrinos. 401 00:39:14,950 --> 00:39:18,820 We have a few other tracers, but almost all of our information comes from photons. 402 00:39:19,480 --> 00:39:28,810 So there's no way to do controlled experiments or externally verify many of the things we believe in astrophysics, and yet we know a huge amount. 403 00:39:29,410 --> 00:39:31,780 And in another talk that I give, 404 00:39:31,780 --> 00:39:38,410 I talk about how do we know that there are black holes in the universe is expanding and there's dark matter because all of those things seem so crazy. 405 00:39:38,420 --> 00:39:44,499 How can you know those things? And yet we know those things with immense confidence and we know those things with immense confidence because there's 406 00:39:44,500 --> 00:39:50,530 many different lines of evidence that show us that that those are the natural explanations of what we're seeing. 407 00:39:50,830 --> 00:39:56,080 But no ones like how the black hole into the High Bay to check it out. 408 00:39:58,630 --> 00:40:03,430 And we know that planets are common and we know that our solar system is not obviously typical of those things. 409 00:40:03,430 --> 00:40:09,160 We know very, very confidently, even though we can't do any, they're never going to go there. 410 00:40:09,190 --> 00:40:12,640 Sorry. Breakthrough Starshot. Whatever [INAUDIBLE] that thing is. 411 00:40:13,000 --> 00:40:16,180 Oh, I'm not supposed to say things like that. And it's Oxford. 412 00:40:16,420 --> 00:40:20,410 And again, this is my last slide. What do I want you to take home? 413 00:40:21,640 --> 00:40:25,209 I want to take home a few things. Planets are plentiful around other stars. 414 00:40:25,210 --> 00:40:31,540 It is a it is not a it's not they're not it's not something like, oh, wow, this star has a plant. 415 00:40:31,540 --> 00:40:39,130 Oh, it's so cool that the sun has planets now. It's totally generic in the sun and planets and it looks like planets are as numerous as stars. 416 00:40:39,520 --> 00:40:44,139 And many of the planets we find are very, very different from the planets we're used to. 417 00:40:44,140 --> 00:40:48,670 And our picture of how solar systems form was just completely up ended. 418 00:40:48,670 --> 00:40:54,580 In fact, there now when I was when I started in graduate school, there was a picture of how the solar system formed. 419 00:40:54,850 --> 00:40:57,429 Now there's no picture of how the solar system formed. 420 00:40:57,430 --> 00:41:01,510 We just lost it because it was just completely ruled out by the first plants that were discovered. 421 00:41:03,910 --> 00:41:07,550 And, you know, maybe that come somehow comes back to this question alone. 422 00:41:07,560 --> 00:41:15,549 But we also only have one universe anyway, and planets are mainly found indirectly. 423 00:41:15,550 --> 00:41:20,560 We do not directly see them, despite what massive press releases might imply to you. 424 00:41:21,070 --> 00:41:24,879 And I think new data science technologies are critical. 425 00:41:24,880 --> 00:41:34,780 And the fact that the world is burgeoning with new astrophysics data and also new data science techniques coming from lots of different directions, 426 00:41:34,780 --> 00:41:38,360 bodes very well for the future of these things. Thank you.