1 00:00:00,000 --> 00:00:04,000 [Music] Ryan Tanner: There are some problems 2 00:00:04,000 --> 00:00:08,000 in astrophysics that can’t be solved on a normal computer. 3 00:00:08,000 --> 00:00:12,000 For this, we use a supercomputer. 4 00:00:12,000 --> 00:00:16,000 [Music] 5 00:00:16,000 --> 00:00:20,000 [Creating Black Hole Jets On A NASA Supercomputer] 6 00:00:20,000 --> 00:00:24,000 [Step 1: Propose a cool research question and work for NASA] 7 00:00:24,000 --> 00:00:28,000 Ryan: My name is Ryan Tanner and I study active galactic 8 00:00:28,000 --> 00:00:32,000 nuclei and star formation, and 9 00:00:32,000 --> 00:00:36,000 how they affect galaxies. I have been doing research 10 00:00:36,000 --> 00:00:40,000 and computer simulations of galactic outflows 11 00:00:40,000 --> 00:00:44,000 for a number of years now. Kim Weaver: My name is Kim Weaver and I work at 12 00:00:44,000 --> 00:00:48,000 NASA. I’ve been looking at AGN for a very long time. 13 00:00:48,000 --> 00:00:52,000 And, we just don’t understand how the jets impact 14 00:00:52,000 --> 00:00:56,000 the galaxies in these low-luminosity objects. [How do weak AGN jets impact their host galaxies?] 15 00:00:56,000 --> 00:01:00,000 Ryan: An AGN 16 00:01:00,000 --> 00:01:04,000 is where you have a supermassive black hole at the center of a 17 00:01:04,000 --> 00:01:08,000 galaxy and occasionally there’s gas or 18 00:01:08,000 --> 00:01:12,000 a star or something that gets too close to the supermassive black hole, 19 00:01:12,000 --> 00:01:16,000 and it begins to get pulled in by gravity, but when it does that, 20 00:01:16,000 --> 00:01:20,000 it releases a huge amount of energy. Kim: There are 21 00:01:20,000 --> 00:01:24,000 strong jets and weak jets. High-intensity AGN, 22 00:01:24,000 --> 00:01:28,000 the large jets, have been studied for a long time. We’ve seen … they’re very obvious, you can see 23 00:01:28,000 --> 00:01:32,000 them on the sky, you see them bright in the radio emission, you see them 24 00:01:32,000 --> 00:01:36,000 bright in the optical. Ryan: The largest ones can get several 25 00:01:36,000 --> 00:01:40,000 million light-years in size. Kim: The low-luminosity 26 00:01:40,000 --> 00:01:44,000 jets are much harder to find, and often do not even 27 00:01:44,000 --> 00:01:48,000 leave their galaxies and are very compact. Ryan: We know some things about the 28 00:01:48,000 --> 00:01:52,000 AGN, but we don’t know everything. It’s like a big puzzle piece; we’re doing a jigsaw puzzle 29 00:01:52,000 --> 00:01:56,000 and there’s pieces that are missing and there’s some that we have fairly well put 30 00:01:56,000 --> 00:02:00,000 together, but others where we have no idea what’s there. One of the major 31 00:02:00,000 --> 00:02:04,000 puzzle pieces that we’re missing is how these 32 00:02:04,000 --> 00:02:08,000 low-luminosity AGNs impact and determine the evolution of 33 00:02:08,000 --> 00:02:12,000 their host galaxies. For this problem we 34 00:02:12,000 --> 00:02:16,000 have to use supercomputers. 35 00:02:16,000 --> 00:02:20,000 [Step 2: Research and write code] Ryan: for my 36 00:02:20,000 --> 00:02:24,000 research I use a code called Athena. It can do anything 37 00:02:24,000 --> 00:02:28,000 from planet formation, star formation, supernovas, 38 00:02:28,000 --> 00:02:32,000 and anything in between, and I did some modifications to 39 00:02:32,000 --> 00:02:36,000 it in order to tailor it to my exact problem 40 00:02:36,000 --> 00:02:40,000 that I have. Kim: The process of watching this was impressive because, you know, there’s 41 00:02:40,000 --> 00:02:44,000 so much computation going in, and there’s so much focus 42 00:02:44,000 --> 00:02:48,000 going in to this project, and the outcome is going to be something 43 00:02:48,000 --> 00:02:52,000 pretty spectacular, but it takes time. 44 00:02:52,000 --> 00:02:56,000 [Step 3: Request supercomputer time] 45 00:02:56,000 --> 00:03:00,000 [Music] 46 00:03:00,000 --> 00:03:04,000 Kim: So Ryan came to me and said “I need a supercomputer” and I said “OK, wha, what?” 47 00:03:04,000 --> 00:03:08,000 [Laughs] because I had never helped anybody get 48 00:03:08,000 --> 00:03:12,000 supercomputer time. I’ve seen it, I’ve been in the room, it’s amazing. 49 00:03:12,000 --> 00:03:16,000 You submit a request online in the system. 50 00:03:16,000 --> 00:03:20,000 Ryan: You have to provide a justification for what you’re going to use. 51 00:03:20,000 --> 00:03:24,000 You have to explain the project behind it, you have to explain 52 00:03:24,000 --> 00:03:28,000 basically what you expect to get out of it and how you will use those 53 00:03:28,000 --> 00:03:32,000 results. And that goes through a review process that can take several months. 54 00:03:32,000 --> 00:03:36,000 The formal review process can take anywhere between six months up to — 55 00:03:36,000 --> 00:03:40,000 I’ve been told 18 months. 56 00:03:40,000 --> 00:03:44,000 [Music] 57 00:03:44,000 --> 00:03:48,000 [Step 4: Access the supercomputer and input your code] Ryan: Basically I can use the supercomputer from anywhere in the 58 00:03:48,000 --> 00:03:52,000 world as long as I have a secure connection to it, and 59 00:03:52,000 --> 00:03:56,000 then once I’m logged onto it it’s as if I’m there using the 60 00:03:56,000 --> 00:04:00,000 supercomputer in the actual building where it’s housed. I can upload 61 00:04:00,000 --> 00:04:04,000 the code that I need to use to run and any 62 00:04:04,000 --> 00:04:08,000 data files that need to go in for inputs, and then I can simply 63 00:04:08,000 --> 00:04:12,000 use a command to submit a job on the supercomputer and 64 00:04:12,000 --> 00:04:16,000 it puts it in a long queue of different people who have submitted jobs on the 65 00:04:16,000 --> 00:04:20,000 supercomputer. 66 00:04:20,000 --> 00:04:24,000 [Music] 67 00:04:24,000 --> 00:04:28,000 [Step 5: Wait] Kim: All I can tell you is there were 68 00:04:28,000 --> 00:04:32,000 days that I asked Ryan how things were going and he said “It’s gonna be another 69 00:04:32,000 --> 00:04:36,000 few days because I have simulations running.” [Laughs] So 70 00:04:36,000 --> 00:04:40,000 for the paper that we wrote for this particular result, it was about 71 00:04:40,000 --> 00:04:44,000 800,000 hours of computational time. 72 00:04:44,000 --> 00:04:48,000 Ryan: When I am actually doing the research and doing the simulations, 73 00:04:48,000 --> 00:04:52,000 like I said, I don’t think too much about it, but when I talk to other people and explain what 74 00:04:52,000 --> 00:04:56,000 I’m doing, that’s when it, I realize oh, this is, these are really big simulations’ 75 00:04:56,000 --> 00:05:00,000 and these are, there’s a lot of stuff going on here that most people will never see 76 00:05:00,000 --> 00:05:04,000 or the amount of computational resources that most people will never use in their life. 77 00:05:04,000 --> 00:05:08,000 [Music] 78 00:05:08,000 --> 00:05:12,000 [Music] 79 00:05:12,000 --> 00:05:16,000 [Step 6: Iterate] Ryan: So, using the supercomputer and getting the 80 00:05:16,000 --> 00:05:20,000 simulation data back was a process I had continuously, would do it over several months. 81 00:05:20,000 --> 00:05:24,000 I would have to run a simulation, download the data, look at it, see if 82 00:05:24,000 --> 00:05:28,000 it was doing what I expected it do. If there was anything that was wrong I had 83 00:05:28,000 --> 00:05:32,000 to go back and figure out what was wrong with the code, then I would make that 84 00:05:32,000 --> 00:05:36,000 change to the code on the supercomputer, run the simulation again, 85 00:05:36,000 --> 00:05:40,000 download the data, look at it, and then just repeat the process over many, many months. 86 00:05:40,000 --> 00:05:44,000 over basically a year. Kim: Fact is, sometimes it can take decades to make 87 00:05:44,000 --> 00:05:48,000 things happen, but you have to have the patience. And that’s, hopefully that’s 88 00:05:48,000 --> 00:05:52,000 what most astronomers have, and they need, is patience. 89 00:05:52,000 --> 00:05:56,000 Because the universe doesn’t always bend to your will, and you have to 90 00:05:56,000 --> 00:06:00,000 wait to find out what’s going on. 91 00:06:00,000 --> 00:06:04,000 [Music] 92 00:06:04,000 --> 00:06:08,000 [Music] 93 00:06:08,000 --> 00:06:12,000 [Step 7: Analyze the results] 94 00:06:12,000 --> 00:06:16,000 Ryan: It was one of those things where I didn’t know 95 00:06:16,000 --> 00:06:20,000 exactly what would come out of the simulations, that’s why we're 96 00:06:20,000 --> 00:06:24,000 doing it. And that’s sort of 97 00:06:24,000 --> 00:06:28,000 the case as it was here, was: we run the simulations and then something unexpected 98 00:06:28,000 --> 00:06:32,000 came out. Oh these AGN, these low-power AGNs, 99 00:06:32,000 --> 00:06:36,000 actually have a very large impact on their host galaxies. 100 00:06:36,000 --> 00:06:40,000 Kim: The blue color in the center, the blue-green colors, indicate the 101 00:06:40,000 --> 00:06:44,000 galaxy itself. When you see the pink colors 102 00:06:44,000 --> 00:06:48,000 that are coming out, and the purple, that is the actual jet itself. 103 00:06:48,000 --> 00:06:52,000 Ryan: The jets can be split. They can be split by 104 00:06:52,000 --> 00:06:56,000 a dense cloud into streams as they come out of the galaxy. 105 00:06:56,000 --> 00:07:00,000 They can be deflected, which is they change 106 00:07:00,000 --> 00:07:04,000 direction entirely, and in some cases the jet can be entirely 107 00:07:04,000 --> 00:07:08,000 stopped. Kim: Are we seeing star formation that’s being created, 108 00:07:08,000 --> 00:07:12,000 by the jets impacting the clouds? Which I think would be really cool, because again 109 00:07:12,000 --> 00:07:16,000 that says the black holes are doing something amazing 110 00:07:16,000 --> 00:07:20,000 to the galaxies by being there. Or we can see that 111 00:07:20,000 --> 00:07:24,000 the jets could be disrupting the star formation and slowing it down 112 00:07:24,000 --> 00:07:28,000 which is another possibility, and those two things, we don’t know yet 113 00:07:28,000 --> 00:07:32,000 which one is the right answer. Ryan: The two major things to draw out of this 114 00:07:32,000 --> 00:07:36,000 is just how complex these jets are because they interact with their 115 00:07:36,000 --> 00:07:40,000 host galaxy. They’re not simple, they’re not nice, smooth outflows. 116 00:07:40,000 --> 00:07:44,000 The other thing is that these low-power AGN jets ... 117 00:07:44,000 --> 00:07:48,000 the galaxy determines what the jets look like to a greater degree 118 00:07:48,000 --> 00:07:52,000 than has been assumed before. Kim: I never thought I’d be involved in a 119 00:07:52,000 --> 00:07:56,000 project that uses a supercomputer. So to me it was a bit of 120 00:07:56,000 --> 00:08:00,000 an alien thing, and so I was fascinated that we 121 00:08:00,000 --> 00:08:04,000 we actually were doing this. And yes, when I saw 122 00:08:04,000 --> 00:08:08,000 the results that came out, I was impressed. 123 00:08:08,000 --> 00:08:12,000 I was excited. Ryan: Just a very excited thing to 124 00:08:12,000 --> 00:08:16,000 study something that is so incredibly new to everybody. 125 00:08:16,000 --> 00:08:20,000 [Image credits] 126 00:08:20,000 --> 00:08:24,000 127 00:08:24,000 --> 00:08:28,000 128 00:08:28,000 --> 00:08:33,621 [NASA]