
CosmoFlow: Using Deep Learning to Learn the Universe at Scale - ArtWomb
https://arxiv.org/abs/1808.04728
======
mturmon
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
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
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-...](https://hci.iwr.uni-
heidelberg.de/vislearn/inverse-problems-invertible-neural-networks/)

>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.

