 impressed about how much we already learned about space, about the universe, and about our place in the universe, our solar system. But the next speakers will explain us how we can use computational methods to simulate the universe and actually grow planets. The speakers will be Anna Penslin. She is PhD student in computational astrophysics in Tübing and Caroline Kimmich. She is physics master student at Heidelberg University. And the talk is entitled Grow Your Own Planet, How Simulations Help Us Understand the Universe. So hi everyone. It's a cool animation, right? And the really cool thing is that there's actually physics going on there, so this object could really be out there in space, but was created on a computer. So this is how a star is forming, how our solar system could have looked like in the beginning. Thank you for being here and that you're interested in how we make such an animation. Anna and I are researchers in astrophysics and we're concentrating on how planets form and evolve. She's doing her PhD in Tübing and doing my master's in Heidelberg. And in this talk we want to show you a little bit of physics and how we can translate that in such a way that a computer can calculate it. So let's ask a question first. What is the universe? Or what's in the universe? The most part of the universe is something we don't understand yet. It's dark matter and dark energy. And we don't know what it is yet. And that's everything we cannot see in this picture here. What we can see are stars and galaxies. And that's what we want to concentrate on in this talk. But if we can see it, why would we want to watch a computer? Well, everything in astronomy takes a long time. So each of these tiny specks you see here are galaxies, just like ours. This is how the Milky Way looks like. And we're living in this tiny spot here. And as you all know, our earth takes one year to orbit around the sun. Now think about how long it takes for the sun to orbit around the center of the galaxy. It's 400 million years. And even the star formation is 10 million years. We cannot wait 10 million years to watch how a star is forming, right? That's why we need computational methods or simulations on a computer to understand these processes. So when we watch to the night sky, what do we see? Of course, we see stars and those beautiful nebulas. They are gas and dust. And all of these images are taken with Hubble Space Telescope. So there's one image that doesn't belong in there. But it looks very similar, right? This gives us the idea that we can describe the gases in the universe as a fluid. It's really complicated to describe the gas in every single particle. So we cannot track every single molecule in the gas that moves around. It's way easier to describe it as a fluid. So remember that for later. We will need that. But first, let's have a look how a star forms. A star forms from a giant cloud of dust and gas. Everything moves in that cloud. So eventually, more dense regions occur and they get even denser. And these clumps can eventually collapse to one star. So this is how a star forms. They collapse due to their own gravity. And in this process, a disk forms. And in this disk, planets can form. So why a disk? As I said, everything moves around in the cloud. So it's likely that the cloud has a little bit of an initial rotation. As it collapses, this rotation gets larger and faster. And now you can think of making a pizza. So when you make a pizza and spin your doll on your finger, you get a flat disk, like a star, like a disk around a star. That's the same process, actually. In this disk, we have dust and gas. From this dust in the disk, the planet can form. But how do we get from tiny little dust particles to a big planet? Well, it somehow has to grow. And grow even further and compact until we have rocks. And even grow further until we reach planets. How does it grow? Well, that dust grows, we know that. At least that's what I observed when I took those images in my flat. Well, so dust can grow and grow even further and compact. But when you take two rocks, we're now at this, in this state, when you take two rocks and throw them together, you do not expect them to stick. You expect them to crash and crack into a thousand pieces. So we're standing on the proof that planets exist. How does this happen? And it's not quite solved yet in research. So this is a process that is really hard to observe, because planets are very, very tiny compared to stars. And even stars are only small dots in the night sky. Also, as I said, planets form in a disk, and it's hard to look inside the disk. So this is why we need computation to understand the process, how planets form and other astronomical processes. So let's have a look at how we simulate it on a computer. Okay. So somehow we have seen nature. It's beautiful. And it's just like a tank of water and a bubbly fluid we already have. So now we have this bubbly fluid in here in the middle demonstrated. But now we have to teach our computer to deal with a bubbly fluid. And that's way too much single molecules to simulate them, as we already said. So there are two ways to discretize it in a way that we just look at smaller pieces. One is the Lagrangian description, just like taking small bubbles or balls of material that have a fixed mass. They have a certain velocity that varies between each particle. And they have, of course, a momentum because they have a velocity and a mass. And we create a number of those particles and then just see how they move around and how they collide with each other. That would be one way. That was described last year in a very good talk. I can highly recommend to hear this talk if you're interested in this method. However, there's a second way to also describe this, not just going with the flow of the particles, but we are a bit lazy. We just box it. So we create a grid. As you see down here, in this grid you have a certain filling level, a bit of a slope. So what's the trend there? And then we just look for each box, what flows in, what flows out through the surfaces of this box. And then we have a volume or a mass filled within this box. And this is how we discretize what is going on in the disk. And actually, since we are usually in the system of a disk, we do not do it in this nice box way like this, but we use boxes like those because they are already almost like a disk. And we just keep exactly the same boxes all the time and then just measure what goes through the surface in these boxes. So this is how these two methods look like if you compute with both of them. So one was done by me. I'm usually using this boxing method and the other was done by my colleague. You see this, like when you look at them at the colors, at the structure, here you have the slope inward. You have the same slope inwards here. You have even this hilly structure here, the same here. But what you notice is you have this enlarged dots that are really, these are really the mass particles we saw before, these bubbles. And here you have this inner cutout. This is because when you create this grid, you have a very region at the inner part of the disk where the boxes become tinier and tinier. And while we can't compute that, so we have to cut out at some point the inner part. So here, when you go to low densities, these bubbles blow up and distribute their mass over a larger area, so it's not very accurate for these areas. And here we have the problem. We can't calculate the inner area. So both methods have their pros and cons and our wallet. But now, for most, we will focus on this one. So we have this nice, actually, stream features. So again, going back to the boxes, we have to measure the flow between the boxes. This flow in physics, we call it flux. And we have a density, row one, a density row two. And the flux is the description of what mass moves through the surface here from one box to the next. So if we write this in math terms, it looks like this. This says the time derivative of the density, meaning the change in the change over time, so how much faster the velocity would be a change in time. And then this weird triangle symbol, it's called nabla, is a positional derivative. So it's like a slope. So how much, how do we change our position, actually? So if we change, look at the density over time, it should correlate to what inflow we have over position. That is what that says. So, and then we have in physics a few principles that we have always to obey because it's just almost common sense. One of them is, well, if we have mass in a box, like this, the mass should not go anywhere unless someone takes it out. So if we have a close box and mass in that box, nothing should disappear magically. We should stay. It should all stay in this box. So even if these particles jump around in our box with a certain velocity, it's the same number of particles in the end. That's, again, the same equation, just told in math. So a second very rudimentary principle is if we have energy in a completely closed box. So, for example, this nice chemicals here, and we have a certain temperature. So in this case, our temperature is low, maybe like outside of around zero degree Celsius, and then we have this nice chemicals down here, and at some point they react very heavily. We suddenly end up with much less chemical energy and a lot more thermal energy. But overall, the complete energy summed up here, like the thermal and the chemical energy, also the energy of the movement and the energy of potential added up to this variable U, that should not change over time if you sum up everything because our energy is conserved within our closed box. And then the third thing is, I think you all know this, if you have like a small mass with a certain velocity, a very high velocity in this case, and it bumps into someone very large, what happens? Well, you get a very small velocity in this large body and the smaller mass stops. And the principle here is that in momentum is conserved, meaning that the velocity times the mass of one object is the same as then later for the other one, but since it's larger, this product has to be the same. That doesn't change. And we have also, like in our simulations, to obey these rules, and we have to code that in so that we have physics in them. So you say, okay, this is really simple, these rules, right? But actually, well, it's not quite as simple. So this is the Navier-Stokes equation. It's a very complicated equation. It's not completely solved. And we have here all that is marked red, other derivatives. Here we have our conservation law that was denies in simple part. But now we have to take other physical things into accounting for pressure, accounting for viscosity, for compression. So squeezing and like how sticky is our fluid and also gravity. So we have a lot of additional factors, additional physics. We also have to get in somehow. And all of these also depend somehow on the change of position or the change of time. And these derivatives aren't really nice for our computers because they, well, they don't understand this triangle. So we need to find a way to write an algorithm so that it can somehow relate with these math formula in a way that the computer likes. And one of the way to do this is, well, the simplest solution actually is just we say, okay, we have now this nasty derivatives and we want to get rid of them. So if we look just at one box now and we say that in this box, the new value for the density in this box would be the previous density plus the flux in and out times the time step over which we measured this flux. Right? So and we have to somehow get to this flux and we just say, okay, this flux now is, if we start here, the slope of this curve, the trend, so to say, where this curve is going right now. So it would look like this. So in our next time step, we would have a density down here. And, well, then we do this again. We again look at this point. Where's the trend going? Where's the line going? And then we end up here. Same here. So again, we just try to find this flux. And this is the trend at this position in time. So this goes up here. And then if we are here now, look at this point, it should go up here. So this is what our next trend would be. And we do this over all the times. And this is how our simulation then would calculate the density for one box over the different time steps. So that kind of works. So the blue curve is the analytical one. The red curve, well, it's kind of simulates it works. But can we do better? It's not perfect yet, right? So what we can do is we refine this a bit, taking a few more steps, making it a bit more computationally heavy, but trying to get a better resolution. So first we start with the same thing as before. We go to this point, find the trend in this point, that point like the line would go in this direction from this point. And then we go just half a step now, sorry. And now we look at this half a step to this point now and again the same saying, okay, where's the trend going now? And then we take where this point would go and add it to this trend. So that would be that the average of this trend, of this exact point and this trend, this dark orange curve. And then we go back to the beginning with this trend now and say this is a better trend than the one we had before. We now use that and go again and search the point for a half a time step. And then again, we do the same thing. Now we again try to find actually the trend and average it with the arrow before. So it's not exactly the trend, it's a bit below the trend because we averaged it with the arrow before. And now we take this averaging trend from the beginning to the top like this. Okay, this is already quite good, but we can still do a little bit better if we average it with our ending point. So we go here, look where is the trend going? That would go quite up like this. And we average this and this together and then we end up with a line like this. This is so much better than what we had before. It's a bit more complicated to be fair, but actually it's almost on the line. So this is what we wanted. So if you compare both of them, we have here our analytical curve. So over time in one box, this is how the density should increase. And now with both of the numerical method, the difference looks like this. So if we have smaller and smaller time steps, even the Euler gets closer and closer to the curve. But actually the Runge-Kutta, this four-step process works much better and much faster. However, it's a bit more computationally difficult. When we simulate objects in astronomy, we always want to compare them to objects that are really out there. So this is a giant telescope consisting of a lot of small telescopes, but they can be connected and used as a giant telescope. And it takes photos of dust in the sky. And this is used to take images of discs around stars. And these discs look like this. So these images were taken last year and they are really cool. Before we had those images, we only had images with less resolution. So they were just blurred blobs. And we could say, yeah, that might be a disc, but now we really see the discs. And we see rings here, thin rings. And we see thicker rings over here. And even some spirally structures here. And also some features that are not really radially symmetric, like this arc here. And it's not completely solved how these structures formed. And to find that out, a colleague of mine took this little object with the asymmetry here. And so this is the imagery just saw. And this is his simulation. So this is how disc looked like in the beginning, probably. And he put in three planets and let the simulation run. And so what we see here is that the star is cut out. As Anna said, we have to, so the grid cells in the inner part are very, very small and it would take a lot of time to compute them all. So that's why we're leaving out that spot in the middle. And what we see here is three planets interacting with the material in the disc. And we can see that these planets can make this thing here appear so that in the end we have something looking very similar to what we want to have or what we really observe. So we can say three planets could explain how these structures formed in this disc. It's a little bit elliptical, you see that. That's because it's tilted from our side of line. It would be round if we watched that it face on, but it's a little bit tilted. That's why it looks elliptical. So we already saw we can put planets in the gas and then we create structures. One very exciting thing that we found in the last year or two years ago, it started, but then we found more, is this system, PDS 70, in this system for the very first time we found a planet that was still embedded with completely within the disc, so the gas and dust. Usually because the gas and dust is the main thing that creates a signal, some radiation because of feet. We only observe that and then we can't observe the planet embedded, but in this case the planet was large enough and in the right position that we actually were able to observe some signature of accretion on this planet that was brighter than the rest of the disc. Then later, just this year, just a few months ago, we actually found out, well, this is not the only object here. This is a very clearly a planet, but actually like this spot here is also something. So we can see it in different grains, like every picture here is a different set of grains observed and we can see this in four different, five different kinds of observations. So there is a planet here and then there's also something we don't know what it is yet, but it's point-like and actually creates a feature that we reproduce in different kinds of observational bands or different kinds of signals here. This is very interesting. For the first time we actually see a planet forming right now within the disc. So a colleague of mine also is very interested in this system and started to simulate how do two planets in the disc change the dynamics of a disc. So here we have, of course, this disc is again tilted because it's not phase-on. It's like 45 degrees tilted, like not like this, but like this. And so he had it phase-on. This is what his simulation looks like. So there are two planets that these blobs here again as in the simulation. Here we have a close-up. You can actually see this little boxes are actually our simulation boxes in which we have our densities. And then he just looked at how the planets would change the structure in the gas and also how the gas would interact with the planets shifting them around. And it's interesting. So the planets tend to clear out an area, open a gap within the disc, block a lot of gas around here so you have a brighter ring here again and then clearing out more and more. And at some point in the simulation he saw they get a bit jumpy. So it's very nice. You also see that the planets induce in the whole disc some kind of features like spiral features. And so a single planet will change the symmetry and the appearance of a whole disc. So the reason why the planet is staying at this point is that because we're rotating with the planet. So it's actually going around the disc but the camera is rotating with the planet. So it's staying at the fixed place we put it in. Exactly. But there's more because as I already said in the Navier-Stokes equation we have a lot of different kinds of physics that we all have to include in our simulations. One of the things of course is we maybe don't have just a star in a disc. We have planets in there and maybe two stars in there and all of these larger bodies have also an interaction between each other. So if we have a star every planet will have an interaction with a star of course but then also the planets between each other they have also an interaction right. So in the end you have to take into account all of these these interactions and then also we have accretion just looking like this. So accretion means that the gas is bound by some object. It can be the disc, the planet or the star that takes up the mass, the dust or the gas and and bounds it to this object and then it's lost to the disc or the other structures because it's completely bound to that. So the principle of this would be a simulation I did last year and published we have here a binary star. So these two dots are stars. I kind of kept them in the same spot but every picture will be one orbit of this binary but since we have interactions you actually see them rotating because of the interactions which is other and then also we have here a planet and here a planet and the interesting thing was that these two planets interact in such a way that they end up on exactly the same orbit. So one starts further out the orange one and then very fast they go in and they end up on exactly the same orbit if it now would play nicely. So another thing is with the accretion here we actually see clouds from above dropping down onto the new forming star here. So all of this what you see here would be gas hydrogen and it's a very early phase so that this is not completely flat it has a lot of material and then you actually have this info from above towards the star and then the star keeps the mass and we have to take this also into account in our simulations. Another thing we have to take into account up till now we just cared about masses and densities but of course what we actually see is that stars are kind of warm hopefully otherwise temperatures here would also not be nice and different chemicals have different condensation points and this is also true in a system so we start with the star temperature at the surface of the star we have a temperature around 4000 Kelvin and then we go a bit into the disk and there's a point where we for the first time reach a point where we have any material at all because it starts to condensate and we actually have something solid like iron for example at 1500 Kelvin and then if we go further in we reach a point where we have a solid water and this is at 200 Kelvin this is what we then would need actually to have a planet that also has water on it because if we don't get the water in the solid state it will not fall onto a terrestrial planet and be bound there right so this is important for our earth actually and then if we go even further out we have also other gases condensating to solids like CO2 or methane or things like that and since we only get water on a planet when we have a temperature that is low enough so that the water actually forms a solid and it's important for us to think about where that is in our forming disk where do we start to have a planet like earth that could have some water right but it's not just the simple picture where we have all these nice ring structures where we have a clear line actually it gets more complicated because we have pressure and shocks and thermodynamics is a lot like pogo dancing right you crash into each other and it's all about collisions so the gas temperature is determined by the speed of your gas molecules like here bouncing and crashing into each other exchanging momentum so there's two ways to heat up such dance first thing is you get a large amount of velocity from the outside like a huge kick a shock into your system and second way would be if we have a higher pressure like more molecules then also you of course have more collisions and then a higher temperature so if you change because you have a planet now in the system the pressure at some point you actually get a higher temperature so that is and not then we lose this nice line because suddenly we have different different pressures at different locations and the colleague of mine also simulated this so it starts also 9th so this is the initial condition we just assumed okay if we have no disturbance whatsoever we have our nice planet here at 1 au so same distance as earth to the sun and here too but here we assume that la less and less heat gets transferred from the surface of the disc and here we have a planet far out like Jupiter or something and now we actually let this planet change the structure of the disc and what happens is we found these spirals and within this virus we change pressure and with this actually if you see this orange everywhere where it's orange it's hotter than the ice line so we don't have water where it's orange and where it's blue we can have water and interesting thing is even if we put a planet out here like Jupiter we still form these regions in the inner system where we have less water and one problem in astrophysical simulations is that we don't always know how to how to shape our boxes or how to or how how small these boxes have to be so we use a trick to reshape the boxes as we need them it's called adaptive mesh and this is a simulation of the red flowing fluid flowing in this direction and the blue fluid in the other one so at the boundary it shape they the two fluids here and they mix up somehow and we don't know how in advance so we start a simulation and as the simulation starts we reshape those boxes here so in the middle we don't need much we reshape because it's not that complicated here it's just a flow but at the boundary we see those mixing up of the two fluids and so we reshape the cells as we need them this is done in some in in a program in an astrophysical program called a repo we will later show you some more programs to to use for simulations but another simulation I want to show you first is also done with a repo and it's a simulation of the universe so from here to here it's very big it's a 30 million light years so each of these dots you see here is the size of a galaxy or even more and here you can actually see that at some regions it's very empty so we're rotating around this universe this simulated universe here and these regions here are empty and we don't need a lot of boxes there the big boxes are enough here but in this dense regions where we have a lot of material we need smaller boxes and this is this method I showed you where we reshape the boxes as we need them is used for this simulation so actually you see it's all the beginning of the universe there basically the initial mass collapsing to the first galaxies and first supernovae starting very beautiful simulation so there are different programs as I already mentioned in astrophysics three of them those three are all open source so you can download them and use them on your own machine if you like and but there are more a lot more some of them open source some of them are not it's sometimes it's hard to to get them we will in the following we will present the two Fargo 3d and Pluto in a detailed version or more detailed version than a repo because yeah we usually yeah we usually use those two for our simulations what I want to show you with this slide is that it depending on what you want to simulate you need to choose a different program and one thing is that in astrophysics we sometimes call the whole program code so if I use the word code sorry about that it's I mean the whole program so let's have a look at Fargo 3d it's a hydrodynamics code and what you see here is an input parameter file there you define how the disk looks like what how much mass does it have how big is it and what planet so here a Jupiter do you see that a Jupiter is put in and we also define how our how big our boxes are this program is written in C which is quite nice because a lot of astrophysical programs are still written in Fortran so this is good for me because I don't know any Fortran we can run this and what's typical for Fargo so that's a compilation actually on my computer so I don't need a fancy computer I just did it on my small laptop and now we run it and now typical for Fargo as you will see are a lot of dots so here it will print out a lot of dots and it will create at certain times some outputs and these outputs are huge files containing numbers so if you look at them they are not really interesting they just are numbers in something like a text file so a big part of astrophysics is also to visualize the data not only to create it but also to make images so that we can make movies out of them for that I prefer to use Python but there are a lot of tools and Python matplotlib but there are a lot of different tools to to visualize the data so this is actually that output the first one we just saw and Jupiter planet in the disk that I defined in this parameter file and it's already started to do some spirals and if I would have let it let it run further than the spirals were more prominent and yeah now we have a planet here on our computer so we also have Pluto Pluto somehow is a bit has a bit more setup files so what I need is three files here looks a bit complicated to break it down this file defines my grid and initial values some and the simulation time here we input actually what physics do we want to need what is our cannot coordinate system so do we want to have a disk or just like spherical boxes or like squared boxes and how is the time defined and here we then actually write a bit of code to say okay now how do I want a gravitational potential so what's the source of gravity or what will happen at the inner region where we have this dark spot we have somehow to define what happens if gas reaches this boundary is it just falling in is this bouncing back or something or is it looping through the one end to the next and this is also something we then just have to code in and if we then make it and let it run it looks like this so again our nice the thing we hopefully put in or wanted to put in the time steps what our boundaries were parameters of physics hopefully the right ones and then nicely we start with our time steps and then we see this it's hooray it worked actually because it's actually not quite simple usually to set up a running program a running problem because you have to really think about what should be the physics what's the scale of your problem what's the time scale of your problem and and specify this in a good way but in principle this is how it works there are few test problems if you actually want to play around with this to make it easy for the beginning and this is how we do simulations so as already said we can just start them on our laptop so here this is my laptop I just type dot slash Fargo 3d and it should run right and then I just wait for ten years to finish the simulations of 500 time steps or something like 500 all outputs well that's not the best idea so we need more power and both of us for example are using and cluster for a button back and and that takes down our computation time by a lot usually like a factor of maybe 20 which is a lot so I would need on my computer maybe a year and then I just need maybe five hours a few days or a week on this cluster which is usually the simulation time about a week for me and so you see here is that we use GPUs yes but we do not or mostly not use them for gaming we use them for actually it's actual science yeah would be nice to play on that right but yeah that just just said so back to our earth actually so can we now we wanted to grow our own planet we can do that yes of course can we grow earth well earth is a very special planet we have a very nice temperature here right and we have not a crushing atmosphere like Jupiter like a huge planet that we could not live under we have a magnetic field that shields us from the radiation from space and we have water but just enough water so that we still have land on this planet where we can live on so even if we fine tune our simulations the probability that we actually hit earth and have all the parameters right it's actually tiny it's not that easy to simulate an earth so and there are a lot of open questions too how did we actually manage to get just this sip of water on our surface how did we manage to collide enough mass or aggregate enough mass to form the terrestrial planet without Jupiter sweeping at all the mass in our system how could we be stable in this orbit when there are seven other planets swirling around and interacting with us all of this is open in our field of research actually and not completely understood this is the reason why we still need to do astrophysics and even in all our simulations there is no planet B and the earth is quite unique and perfect for human life so please take care of the earth and take care of yourself and of all the others people on the Congress and thank you for listening and thank you to everyone who helped us make this possible and to the people who actually coded our programs with which we simulate thank you thank you for the beautiful talk and for the message at the end the the paper is open for discussion so if you guys have any questions please come to the microphones I'm asking my signal angel no questions right now but microphone to please yes thank you very much very beautiful talk I can agree I have two questions the first is you showed you are using Navier stokes equation but you have on the one hand you have the dust disc and on the other hand you have solid planets in it and so are you using the same description for both was it the hybrid it very much depends like this is one of the things I showed you that for Pluto we write this C file that specifies some things and about every physicist has somewhat his or her own version of things and so some usually the planets if they are large they will be put in as a gravity source and possibly one that can accrete and pebbles are usually then put in in a different way however also pebbles are at the moment a bit complicated there are special groups specializing in understanding pebbles because and as we said in the beginning and when they collide usually they should be destroyed if you hit two rocks very hard together they are two rocks together they don't stick if you hit them hard together there's platter around and we don't add up with explain pebbles are a small rocks are like big sandstones or something like that yeah so bigger bigger rocks but not very big yet so yes so it depends on which code you use yes very short maybe one do you also need to include relativistic effects or is that completely out it's a good question and mostly if you have a solar type system you're in a range where it this is not necessary for example with the binaries if they get very close together then at the inner part of this that is something we could consider and actually I know for Pluto it has modules to include relativistic physics to yes thank you okay we have quite some questions so keep them short number one please thank you yeah thank you very much for your interesting talk I think you had it on your very first slide that about 70% of the universe consists of dark matter and energy is that somehow considered in your simulations or handle this well in the simulations we make we're doing planets and discs around stars it's not considered there in this simulation we showed you about the universe at the beginning the bluish things where all dark matter so that was included in there okay thank you okay microphone three oh hi thanks sorry I think you talked about three different programs I think Pluto Fargo 3d and a third one I was running say you're a complete beginner which program would you suggest is like you more use like if you want to learn more which one is user-friendly you're good I would suggest Fargo first it's kind of user-friendly has somewhat good support and they are always also always very thankful for actual comments and additions if people actually are engaged in trying to improve on that because we are physicists we're not perfect programmers and we're all also happy to learn more so yeah Fargo I would I would suggest it has some easy ways of testing some systems and getting something done and it also has a very good documentation and also an yeah a manual how to make the first steps on the internet so you can look that up awesome thank you let's get one question from outside from my signal angel thank you for your talk and there's one question from ISC how do you know your model is good and when you can only observe snapshots oh that's a good question we all we have to as we said we're in theoretical astrophysics so there are theoretical models and these models cannot include everything so every single process it's not possible because then we would calculate for years and yeah to to know if a model is good you have to usually you have a hypothesis or an observation that you somehow want to understand with most of the necessary physics at the stage to reproduce this image so also from the observation we have to take into account what our parameters kind of should be how dense this end of the simulation should be and things like this so by comparing to observations that's the best measure we can get if we kind of agree of course if we do something completely wrong then it will just blow up or we will get a horribly high density so this is how we know it's physics will just go crazy we do too too large mistakes otherwise we would try to compare to observations that it actually is sensible what we did yeah that's one of the most complicated tasks to include just enough physics that the system is represented in in a good enough way but not yeah so but not too much that our simulation would blow up in time number two please I've got a question about the adapted grids how does the computer decide how to adapt the grid because the data where the high density comes after making the grid this is actually quite an interesting and also not quite easy to answer the question let me try to give a breakdown nutshell answer here the thing is you measure and evaluate the velocities or in the flux you also evaluate the velocity and if the velocity goes high you know there's a lot happening so we need a smaller grid than there so we try to create more grid cells where we have a higher velocity in a nutshell this is of course in an algorithm a bit harder to to actually achieve but this is the idea we measure the velocities at each point and then if we measure a high velocity we change to a smaller grid so you can predict where the mass will go and where the densities are getting high exactly step by step so to say thanks we stay with microphone to okay I've got a bit of a classical question so I guess a lot relies on your initial conditions and I have two questions related to that so first I guess they are inspired by observations what are the uncertainties that you have and be them what is the impact if you change your initial conditions like the density in the disk yeah I'm I right now my main research is actually figuring out a sensible initial conditions or parameters for a disk if you just let it have an initial set of conditions and a sensible set of parameters and let it run very long you expect a system hopefully to converge to the state that it should be in but your parameters are of course very important and here we go back to what we can actually understand from observations and what we need for example is the density for example and that is something we try to estimate from the light we see in these discs that you saw in this nice grid with all these discs we estimate okay what's the average light there what should then be the average densities of dust and gas in comparable discs okay thanks okay one more at number two yes thank you for the talk when you increase the detail on the grid and you learn model and you have to when you want to compute the the gravitational force in one cell you have to sum the all the masses from all the other cells so the complexity of the calculus grows quite right tically at the square of the how do you solve that or just put more CPUs well that would be one way to do that but you there are ways to simplify if you have a lot of particles in one direction and they are far away from the object you're looking at and so yeah so if you have several balls here in one ball here then you can include all these balls or you can you can think of them as one ball so it depends on you look at so how you define how many particles you can take together is when you look at the angle of this of the yeah the big or the many particles will have from seen from the object you're looking at and you can define a critical angle and it if it's if an object gets smaller than this or if a lot of objects get smaller than this angle you can just say okay that's one object so that's a way to simplify this method and there are some yeah I think that's the main idea okay we have another one do you have a strategy to check if the simulation will give a valuable solution or does it happen a lot that you wait one week for the calculation and find out okay it's total crash that trash or it crashed in the time so that also depends on the program you're using so in Fargo it gives these outputs after a certain amount of calculation steps and you can already look at those outputs before the simulation is finished so that would be a way to to control if it's really working yeah but I think it's the same for Pluto so you you get every you set there's a difference between time steps and actually output steps so and you would define your output steps not as the whole simulation but you can look at each output step as soon as it's produced so I usually get like 500 outputs but I already can look at the first and second after maybe half an hour or something like that yeah but it also happens that you start a simulation and wait and wait and wait and then see you put something wrong in there and well then you have to do it again so this happens as well thanks okay one final question yeah okay is there a program in which you can calculate it backwards so that you don't have the starting conditions but the the ending conditions and you can calculate how it had started and not for hydrodynamics if you go to anybody there is a way to go backwards in time but for hydrodynamics the thing is that you have turbulent almost like chaotic conditions so you you cannot really turn them back in time with anybody kind of because actually it's kind of well it's not analytically solved but it's much closer than like turbulence is dreams spirals and all the things you you saw in the simulations okay I guess that brings us to the end of the talk and of the session thank you for the discussion and of course thank you guys for the presentation