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CosmoFlow: Using Deep Learning to Learn the Universe at Scale

60 points by ArtWomb 8y ago

mturmon 8y ago
I took a quick look because I'm aware of the work that has gone in to producing the best estimates of \Lambda CDM parameters from CMB data. This has been done by large-scale MCMC - not DL at all. But, the CMB signal really is Gaussian, so MCMC is well-suited. By going to the trouble of making a principled forward model including error statistics, you get error bars on all reported parameters, such as the age of the universe - these error bars are critical to later analysis. ("This model requires a Hubble constant in the range ... which is not supported by current observational evidence.") Parameter estimation from large scale structure is not as "easy". OTOH, I wonder about the accuracy of their map from (cosmological parameters) -> (Large scale structure), because that's what their system is inverting. That is, I wonder about their training data. From sec. 4C of the current paper, and comparing to their reference to Ravanbakhsh et al 2017 (ICML, [1]), they seem to be using numerical simulations (N-body sims representing gravitational coalescence). These models might have large systematic errors, and their inverse may have its own problems. Long way to say: I wonder how these estimates of Lambda CDM parameters are viewed by the astrophysics community? The present paper is more of a systems/methods paper than an astrophysics paper. The estimates will come with no error bars, and will be critically based on the simulations used, and the NN limitations. [1] https://arxiv.org/pdf/1711.02033.pdf https://arxiv.org/pdf/1711.02033.pdf
- SiempreViernes 8y ago
  Honestly it seems a bit like stirring the linear algebra pile for the sake of stirring, I don’t think fitting parameters to observation is a space that needs deep neural nets. They talk about their method allowing fast experimentation with how to train dnn, but what is really needed computationally is fast n-body sims to explore the theory space not a fast classifier once you have a fully converged simulation. I just can’t see what role the trained network has in cosmology.
  - grumpopotamus 8y ago
    Recently there was some very interesting work on solving ambiguous inverse problems with Invertible Neural Networks: https://hci.iwr.uni-heidelberg.de/vislearn/inverse-problems-invertible-neural-networks/ https://hci.iwr.uni-heidelberg.de/vislearn/inverse-problems-... >We can for example say that this specific observation y either corresponds to a young cluster with large expansion velocity, or to an older system that expands slowly.
deleted 8y ago
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60 points by ArtWomb 8y ago

I took a quick look because I'm aware of the work that has gone in to producing the best estimates of \Lambda CDM parameters from CMB data. This has been done by large-scale MCMC - not DL at all. But, the CMB signal really is Gaussian, so MCMC is well-suited. By going to the trouble of making a principled forward model including error statistics, you get error bars on all reported parameters, such as the age of the universe - these error bars are critical to later analysis. ("This model requires a Hubble constant in the range ... which is not supported by current observational evidence.") Parameter estimation from large scale structure is not as "easy". OTOH, I wonder about the accuracy of their map from (cosmological parameters) -> (Large scale structure), because that's what their system is inverting. That is, I wonder about their training data. From sec. 4C of the current paper, and comparing to their reference to Ravanbakhsh et al 2017 (ICML, [1]), they seem to be using numerical simulations (N-body sims representing gravitational coalescence). These models might have large systematic errors, and their inverse may have its own problems. Long way to say: I wonder how these estimates of Lambda CDM parameters are viewed by the astrophysics community? The present paper is more of a systems/methods paper than an astrophysics paper. The estimates will come with no error bars, and will be critically based on the simulations used, and the NN limitations. [1] https://arxiv.org/pdf/1711.02033.pdf https://arxiv.org/pdf/1711.02033.pdf

SiempreViernes 8y ago
Honestly it seems a bit like stirring the linear algebra pile for the sake of stirring, I don’t think fitting parameters to observation is a space that needs deep neural nets. They talk about their method allowing fast experimentation with how to train dnn, but what is really needed computationally is fast n-body sims to explore the theory space not a fast classifier once you have a fully converged simulation. I just can’t see what role the trained network has in cosmology.
- grumpopotamus 8y ago
  Recently there was some very interesting work on solving ambiguous inverse problems with Invertible Neural Networks: https://hci.iwr.uni-heidelberg.de/vislearn/inverse-problems-invertible-neural-networks/ https://hci.iwr.uni-heidelberg.de/vislearn/inverse-problems-... >We can for example say that this specific observation y either corresponds to a young cluster with large expansion velocity, or to an older system that expands slowly.

deleted 8y ago

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