Recreating Our Galaxy in a Supercomputer

I appreciate you probably put a lot of effort out but your post largely sums up to "They are wrong, I've done extra curricular research to prove it, I have no alternative"

While noble, I cant help but wanting to understand what alternatives you were leading yourself done. As an example, light can arguably simplified to intersecting cylinders and spheres that bounce off surfaces to create new 3d shapes. Each shape also would have an origin 2d shape based upon whats reflecting it. an "eye" reads shape intersections with self and also can filter those intersections in respect to origin shape. After each bounce, the new shape takes form as the bouncing lights color multiplied by the color of the bounced object In low light situations, subtle luminosity differences can be enhanced.

What I did was offer an example. Perhaps youll one day be successful but I got the impression you are some kind of renegade with a mission. While I can certainly relate to that, I view science and building the future quite far from renegade status. And in the mean time, you gave me a sob story with no algorithms/solutions except for "take real pictures and compare them". As a lazy programmer, walking outside and discoveringvthe world doesnt interest me too much.

Which is why, for nearly all simulations, you use Born-Oppenheimer approximation and molecular dynamics using force fields derived from QM, but without the full explicit QM treatment.

This is fine because with only rare examples (as far as modern science knows), QM treatment is not necessary for most molecular machines.

> The main problem with these kind of simulations is that they're O(N^2). You can't get away with modelling each star, you have to model the interaction of each star with every other star.

Summary of what's below: The type of simulations described in the article do not scale like O(N^2) but more like O(N log N).

A small correction here.. The gravitational force calculation for N-body simulations are only O(N^2) if they're done using direct summation and force calculation. That's only necessary if you care about the motions of individual stars. The largest simulations I'm aware of that do that use a few 10s or 100s of millions of stars. They're typically used for studying individual star clusters, the regions around supermassive black holes, and perhaps dwarf galaxies. But the simulation in this article/press release has a few 10s of billions of particles, I believe. And those particles are all much more massive than individual stars. So while there are "star particles" in the simulations, it's more accurate to think of them as a set of particles which constitute a monte carlo sampling of the stellar mass distribution.

One aspect of that is you now no longer care about the detailed trajectories of individual particles[0], instead caring about the overall representation of the mass distribution. So you don't need as high of accuracy in the force calculation. Going back to the 1980's, people have been using numerical tricks to reduce the scaling of the simulations with the particle number. This includes algorithms such as treecodes, particle-mesh codes, etc., which reduce the scaling to O(N log N) or even O(N). These are sufficient to run simulations of describing the overall gravitational field.

The scaling for hydro codes is probably more complicated, particularly for simulations which use a moving mesh.

There's a nice summary of numerical techniques for this type of work here: http://arxiv.org/abs/1412.5187

[0] One can determine if interactions between individual stars is important to a system's evolution by comparing the 2-body relaxation time to the age of the system. This is discussed on pages 8–9 of the preprint linked above.

I'm always a bit uncomfortable when I read about findings from these simulations. To say they have "answered questions" about galactic evolution seems to strong a statement. How about, "created a more accurate model"?

> "We use X technique because it looks good."

Replaces "looks good" with a more generic "gets the right answer" and you'll notice that phrase repeated on any simulation context where computers are not powerful yet to work from first principles.

If you want a safe place to look, look at chemistry. They have simulations that vary in complexity by a huge number of orders of magnitude. You'll see those non-natural techniques that "look good" get progressively applied as the simulation gets slower and slower. It works really well, but once computers are fast enough, everybody just throws them by the window.