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The title is misleading in an important way. What ML algorithm has found out that from properties of a single _simulated_ galaxy it was possible to deduce the p

by _0w8t 5y ago

The title is misleading in an important way. What ML algorithm has found out that from properties of a single _simulated_ galaxy it was possible to deduce the properties of а _simulated_ universe. So at this point it is not clear if this relation is an artifact of the simulation or there is a real dependency.

klysm 5y ago
ML model made homogenous output.
ianai 5y ago
The cosmological background radiation, which is an image of the universe shortly after the big bang and before inflation, looks similar to the current layout of matter across the universe. This would seem consistent with a universe being able to be extrapolated from a region of it if there's some underlying holographic relationships. Based on this clip from David Tong. Starts here and goes on a few minutes: https://youtu.be/zNVQfWC_evg?t=2714 https://youtu.be/zNVQfWC_evg?t=2714
lucb1e 5y ago
That's addressed in the article: > The team spent half a year trying to understand how the neural network had gotten so wise. They checked to make sure the algorithm hadn’t just found some way to infer the density from the coding of the simulation rather than the galaxies themselves.
- nieve 5y ago
  Except that they acknowledge its performance was dependent on the details of the simulation and failed with a different recipe for making universes, so in fact it was dependent on not just the code, but the settings.
yuvalr1 5y ago
You're right to say the title is misleading. It is touched in the very end of the article: > But in practice, the technique would have to first overcome a major weakness. The CAMELS collaboration cooks up its universes using two different recipes. A neural network trained on one of the recipes makes bad density guesses when given galaxies that were baked according to the other. The cross-prediction failure indicates that the neural network is finding solutions unique to the rules of each recipe
sdenton4 5y ago
The title tells us that there are results for multiple universes, which clearly implies that they're working with simulated data...

by _0w8t 5y ago

klysm 5y ago

ML model made homogenous output.

ianai 5y ago

The cosmological background radiation, which is an image of the universe shortly after the big bang and before inflation, looks similar to the current layout of matter across the universe. This would seem consistent with a universe being able to be extrapolated from a region of it if there's some underlying holographic relationships. Based on this clip from David Tong. Starts here and goes on a few minutes: https://youtu.be/zNVQfWC_evg?t=2714 https://youtu.be/zNVQfWC_evg?t=2714

lucb1e 5y ago

That's addressed in the article: > The team spent half a year trying to understand how the neural network had gotten so wise. They checked to make sure the algorithm hadn’t just found some way to infer the density from the coding of the simulation rather than the galaxies themselves.

nieve 5y ago
Except that they acknowledge its performance was dependent on the details of the simulation and failed with a different recipe for making universes, so in fact it was dependent on not just the code, but the settings.

yuvalr1 5y ago

You're right to say the title is misleading. It is touched in the very end of the article: > But in practice, the technique would have to first overcome a major weakness. The CAMELS collaboration cooks up its universes using two different recipes. A neural network trained on one of the recipes makes bad density guesses when given galaxies that were baked according to the other. The cross-prediction failure indicates that the neural network is finding solutions unique to the rules of each recipe

sdenton4 5y ago

The title tells us that there are results for multiple universes, which clearly implies that they're working with simulated data...