*36c3 preroll music*
Herald: Ok, I have to say, I'm always deeply 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 Penzlin (miosta). She is PHC student in computational
astrophysics in Tübingen and Carolin Kimmich (caro). She is a physics master's
student at Heidelberg University. And the talk is entitled "Grow Your Own Planets
How Simulations Help us understand the universe." Thank you!
*applause*
caro: 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 and in Tübingen and I'm doing my masters 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 specs you see here are galaxies just like ours. This is how the Milkyway
looks like. And we are 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 four hundred 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 a gas and dust. And all of these images are taken with Hubble Space
Telescope. Oh, so there's one image that does 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 stars form. 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 clams 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 disc forms. And in this disc, planets can form.
So why a disc? 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 dough on your finger, you get a flat disc like a star,
like a disc around a star. That's the same process, actually. In this disc, we have
dust and gas. From this dust in the disc 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 stage. When you take two rocks and throw them together, you don't expect them
to stick, right? 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 disc. And it's
hard to look inside the disc. So this is why we need computation to understand a
process that how planets form and other astronomical processes. So let's have a
look at how this simulated on a computer. miosta: OK. 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 and here in the middle demonstrated.
But now we have to teach our computer to deal with the 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've created 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. And 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. And as you see down here in this grid, you have the
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 disc. And actually, since we are usually in the
system of a disc, 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 disc and we just keep exactly the
same boxes all the time and you 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 inwards, you have the same slope
inwards here. You have even this silly structure here. The same here. But what
you notice is you have this enlarge dots that 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 the very region at the inner part of the disc where
the boxes become tiny and tinier. And well, we can't compute that. So, we have
to cut out at some point in 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 are valid. But now, for most we
will focus on this one. Just so we have this nice 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, density row too.
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 over time. So how much
faster or slower, 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 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 that is just almost common sense. One of them is, well, if we
have mass in a box. Well, like this, the mass should not go anywhere unless someone
takes it out. So, if we have a closed box and mass in that box, nothing should
disappear magically. 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 it, 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 clothed 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 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 in our simulations to obey these rules and we
have to code that in so that we have physics in them. So you say, ok, this is
really simple, these rules, right? But actually, well, it's not quite as simple.
So, this is the Navier-Stokes equation, a very complicated equation is not
completely solved. And we have here all that is marked red are derivatives. Here
we have our conservation law that was the nice and 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, OK, 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 stepover which we measure this flux,
right? So, and we have to somehow get to this flux and we just say, OK, this flux
now is if we start here and the slope of this curve, the trends so to say, where
this curve is going right now, it would look like this. So, in our next step, 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 flax 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 a different time steps. So, that kind of
works. So, the blue curve is the analytical one, the red curve, well it
kind of similar, 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, OK, where's the trend going now?
And then we take where this point would go and added 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 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 averaged 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 densities should
increase. And now with it 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
and difficult. caro: When we simulate objects in
astronomy, we always want to compare that to objects that are really out there. So,
this is a giant telescope, well 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 spiraly structures here. And also some features that are not really
radial 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 image we just saw. And this is his
simulation. So, this is how the disc looked like in the beginning, probably.
And we 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. So, the grid cells in the inner part are
very, very small. And it would take a long 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 you watched at it face on. But it's a little bit tilted. That's
why it looks elliptical. miosta: 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 completely within the disc. So, the gas and dust. Usually,
because the gas and dust is the main thing that creates this signal of some radiation
because of heat. 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. And then later, just this year, just a few
months ago, we actually found out well, this is not the only object here. This is
very clearly a planet. But actually, like this spot here is also something. So,
we can see it in different grains. Every picture here is a different set of grains
observed. And we can see this in five different kinds of
observations. So, there is a planet here. And then there is also something we don't
know what it is yet, but its point like and actually creates the 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. And so a colleague of mine also is very
interested in the system and started to simulate how do two planets in a 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, not like this, but like
this. And so he had it face on. This is what a simulation looks like. So, there
are two planets: these blobs here, again, as in this simulation. Here we have a
close up. You can actually see this little boxes are actually our simulation boxes in
which we have our own densities. And then he just looked at how the planets would
change the structure and 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, and within the disk, that block has a lot of gas around
here. So, you have the brighter ring here again and then clearing out more and more.
And at some point in the simulation you saw they get a bit jumpy. So it's very nice.
You also see that 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. caro: So, the reason why the planet is
staying at this point is because we're rotating with the planet. So it's actually
going around the disc, but the like camera is rotating with the planet. So, it's
staying at that fixed place we put it in. miosta: 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 and 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 the star, every planet will have an
interaction with the 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 interactions. And then also we have accretion just looking like this.
So, accretion means that the gas is bound by some objects. It can be the disc, the
planet or the star that takes up the mass, the dust or the gas and bounce 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 the 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, with each 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
star's further out, the orange one, and then very fast they go in. And they end up on
exactly the same orbit. If it now 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 disc is not completely flat. It has a lot of material. And then we
actually have this infall 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 start temperature at the surface of the star. We have a
temperature around 4.000 Kelvin. And then we go a bit into the disc. And there is 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 a 1500 Kelvin. And then if we go further in, we reach a point where
we have 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 is solid and it's
important for us to think about where that is in our forming disc. Where do we start?
We 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 you 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. A
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 not then we lose this nice line
because suddenly we have different pressures at different locations. And a
colleague of mine also simulated this. So, this is the initial condition we
just assumed: OK, if we have no disturbance whatsoever, we have our nice
planet here at 1au. So, same distance as earth to the sun. Here, too. But here we
assume that less heat gets transferred from the surface of the disc. And here we
have the 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 these spirals, we change pressure. And with this actually, if you
see this orange, everywhere where it's orange it's hotter than the iceline. So,
we don't have water where it's orange. And where it's blue we can have water. And the
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.
caro: One problem in astrophysical simulations is that we don't always know
how to shape our boxes or 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 fluid flowing in this direction and the blue fluid in the
other one. So, at the boundary, the two fluid shear 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 the 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 a program, in an astrophysical program called AREPO. We
will later show you some more programs to use for simulations. But another
simulation I want to show you first is also done with AREPO and it's a simulation
of the universe. So, from here to here, it's very big. It's 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 method I showed you where we reshape the boxes as we need them is
used for this simulation. miosta: So, actually, you see the
beginning of the universe there. caro: Yes!
miosta: Basically, the initial mass collapsing to the first galaxies and first
supernovae starting. Very beautiful simulation.
caro: 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. But there are more, a lot more. Some of them
are open source, some of them are not. Sometimes it's hard to get them. In the
following, we will present the tool FARGO3D and PLUTO in a detailed version or
a more detailed vision than AREPO because we usually use those two for our
simulations. What I want to show you with this slide is that 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. I mean, the whole program. So, let's have a look at FARGO3D.
It's a hydro dynamics code and what you see here is an input parameter file. There
you define how the disc looks like. How much mass does it have? How big is it? And
what planet? So, here at Jupiter, do you see that? Jupiter is put in. And we also
define 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 FARGO3D. 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. Now,
typical for FARGO3D, as you will see are 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 a 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 Python Matplotlib, but there are a lot of
different tools to visualize the data. So, this is actually that output. That first
one we just saw. The Jupiter planet in the disc that I defined in this parameter file
and it's already started to do some spirals. And if I would have let it
run further than the spirals were more prominent. And yeah, now we have a planet
here on our computer. miosta: OK, so we also have PLUTO. PLUTO
somehow 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.
And this simulation time here we input actually what physics do we want to need?
What is our coordinate system? So, do we want to have a disc 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, OK, 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 it bouncing back or something? Or
is it looping through the one end to the next? This is also something we then just
have to code in. And if we then make it and let run, it looks like this. So,
again, our nice 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 timescale of your
problem? 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 I already
set, we can just start them on our laptops. So, here this is my laptop. I
just type a dot slash FARGO3D and that should run, right? And then I just wait
for ten years to finish the simulations of 500 timesteps or outputs. Well, that's not the best
idea. So, we need more power. And both of us, for example, are using a cluster for
Baden-Württemberg 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 5 hours, a few days or a week on this cluster, which is
usually the simulation time about a week for me.
caro: So, what 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 actual science. Yeah, would be nice to
play on that, right? That just said! miosta: 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
simulations, the probability that we actually hit Earth and have all the
parameters right is actually tiny. It's not that easy to simulate an Earth. 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 this terrestrial planet without Jupiter is
sweeping up 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!
*applause*
Herald: Thank you for the beautiful talk and for the message at the end, 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 two
please! Mic2: Oh, yeah. Thank you very much.
Really beautiful talk. I can agree. I have two questions. The first is short. 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 or is it a hybrid?
miosta: It very much depends. 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. 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 a different way. However, also pebbles are
at the moment a bit complicated. There are special groups specializing in
understanding pebbles because as we said in the beginning, when they collide,
usually they should be destroyed. If you hit two rocks very together, they don't
stick. If you hit them hard together, they splatter around and we don't end up with an bigger object
caro: Just to explain pebbles are small rocks or like big sand stones or something
like that. Yeah. So bigger rocks, but not very big, yet.
miosta: Yes! caro: It depends on which code you use.
Mic2: Thank you. Very short, maybe one. Do you also need to include relativistic
effects. Or is that completely out? miosta: It's a good question. Mostly if
you have a solar type system, you're in the arrange where this is not necessary.
For example, with the binaries, if they got very close together, then at the inner
part of the disc, that is something we could consider. And actually, I know for
PLUTO, it has modules to include relativistic physics, too, yes!
Mic2: Thank you! Herald: OK, we have quite some questions,
so keep them short. Number one, please! Mic1: Thank you. Yeah. Thank you very
much for your interesting talk. And I think you had it on your very first slides
that about 70 percent of the universe consists of dark matter and energy. Is that
somehow considered in your simulations or how do you handle this?
caro: Well in the simulations we make, we are doing planets and discs around stars.
It's not considered there. In the simulation we showed you about the
universe at the beginning, the blueish things were all dark matter. So, that was
included in there. Mic1: OK, thank you.
Herald: OK. Microphone 3. Mic3: Hi, thanks. Sorry, I think you
talked about three different programs. I think PLUTO, FARGO3D and a third one. So,
for 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 or good?
miosta: I would suggest FARGO3D first. It's kind of user friendly, has a somewhat good
support and they are always also 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 also happy to learn more. So
yeah, FARGO3D I would suggest, it has some easy ways of testing some systems and
getting something done. caro: And it also has a very good
documentation and also a manual "How to make the first steps on the Internet". So,
you can look that up. Mic3: Awesome. Thank you.
Herald: Let's get one question from outside, from my Signal Angel.
Signal Angel: Thank you for your talk. There's one question from IRC: How do you
know your model is good when you can only observe snapshots?
caro: Oh, that's a good question. 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.
Yeah, to know if a model is good you have to…
miosta: Usually, you have a hypothesis or an observation that you somehow want to
understand. With most of the necessary physics at this stage to reproduce this
image. So, also from the observation we have to take into the account what our
parameters kind of should be, how dense this end of the simulation should be and
things like this. So, by comparing two 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. Physics will just go crazy if we do too large mistakes. Otherwise, we would
try to compare two observations that it actually is sensible what we did.
caro: Yeah, that's one of the most complicated tasks to include just enough
physics that the system is represented in a good enough way. But not too much that
our simulation would blow up in time. Herald: Number two, please.
Mic2: I've got a question about the adaptive grids. How does the computer
decide how to adapt the grid? Because the data where's the high density comes after
making the grid... miosta: Yes, this is actually quite an
interesting and also not quite easy to answer 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 then
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 actually
achieve. But this is the idea. We measured the velocities at each point. And then if
we measure a high velocity, we change to a smaller grid.
Mic2: So, you can predict where the mass will go and whether densities are getting high.
miosta: Exactly. Step by step so to say.
Mic2: Thanks Herald: We stay with Microphone 2.
Mic2: 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 B, then what is the impact if you change your initial conditions like the
density in the disc? miosta: Yeah, right now my main research
is actually figuring out a sensible initial conditions or parameters for a
disc. 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 convert 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 OK, what's the average light there? What should then be
the average densities of dust and gas in comparable disks.
Mic2: Okay, thanks. Herald: Okay, one more at number two.
Mic2: Yes. Thank you for the talk. When you increase the detail on the grid and
you learn more. When you want to compute the gravitational force in one cell, you
have to somehow hold masses from the all the other cells. So, the complexity of the
calculus grows. miosta: Yes
Mic2: Quadratically, at the square of the... how do you solve that? With more CPUs?
caro: Well, that would be one way to do that. But 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. So, yeah. So, if you have several balls here and one ball
here, then you can include all these balls or you can think of them as one ball. So,
it depends on how you look at it. So, how you define how many particles you can take
together is when you look at the angle of this... many particles we'll have from the
seen from the object you're looking at. And you can define a critical angle. And
if an object gets smaller or if lot of objects get smaller than this angle, you
can just say, OK, 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.
Herald: Okay, we have another one. Mic2: 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 OK it's total trash or it crashed in
the time. caro: So, that also depends on the program
you're using. So, in FARGO3D, 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 control if it's really working. Yeah, but
I think... miosta: It's the same for PLUTO. So, there
is a difference between timesteps and actually output steps. So and you could
define your output steps not and 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.
caro: 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.
Mic2: Thanks. Herald: Okay. One final question.
Mic2: Yeah, OK. Is there a program in which you can calculate it backwards? So
that you don't have the starting conditions but the ending conditions
and you can calculate how it had started? miosta: Not for hydrodynamic. If you go to
n-body, there is a way to go backwards in time. But for hydrodynamics, the thing is
that you have turbulent and almost like chaotic conditions. So, you cannot really
turn them back in time. With n-body you can it because actually it's kind of... Well,
it's not analytically solved, but it's much closer than like turbulences,
streams, spirals and all the things you saw in the simulations.
Herald: OK, 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.
*36c3 postroll music*
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