Super-resolution emulator of cosmological simulations using deep physical models. (arXiv:2001.05519v1 [astro-ph.CO])
<a href="http://arxiv.org/find/astro-ph/1/au:+Ramanah_D/0/1/0/all/0/1">Doogesh Kodi Ramanah</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Charnock_T/0/1/0/all/0/1">Tom Charnock</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Villaescusa_Navarro_F/0/1/0/all/0/1">Francisco Villaescusa-Navarro</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wandelt_B/0/1/0/all/0/1">Benjamin D. Wandelt</a>
We present an extension of our recently developed Wasserstein optimized model
to emulate accurate high-resolution features from computationally cheaper
low-resolution cosmological simulations. Our deep physical modelling technique
relies on restricted neural networks to perform a mapping of the distribution
of the low-resolution cosmic density field to the space of the high-resolution
small-scale structures. We constrain our network using a single triplet of
high-resolution initial conditions and the corresponding low- and
high-resolution evolved dark matter simulations from the Quijote suite of
simulations. We exploit the information content of the high-resolution initial
conditions as a well constructed prior distribution from which the network
emulates the small-scale structures. Once fitted, our physical model yields
emulated high-resolution simulations at low computational cost, while also
providing some insights about how the large-scale modes affect the small-scale
structure in real space.
We present an extension of our recently developed Wasserstein optimized model
to emulate accurate high-resolution features from computationally cheaper
low-resolution cosmological simulations. Our deep physical modelling technique
relies on restricted neural networks to perform a mapping of the distribution
of the low-resolution cosmic density field to the space of the high-resolution
small-scale structures. We constrain our network using a single triplet of
high-resolution initial conditions and the corresponding low- and
high-resolution evolved dark matter simulations from the Quijote suite of
simulations. We exploit the information content of the high-resolution initial
conditions as a well constructed prior distribution from which the network
emulates the small-scale structures. Once fitted, our physical model yields
emulated high-resolution simulations at low computational cost, while also
providing some insights about how the large-scale modes affect the small-scale
structure in real space.
http://arxiv.org/icons/sfx.gif
