A volumetric deep Convolutional Neural Network for simulation of mock dark matter halo catalogues

Apr 26, 2019 - 9:30 am to 10:30 am
Location

SLAC, Kavli 3rd Floor Conf. Room

Speaker
Dongwoo Chung

 

Join us Friday, April 26th for the latest KIPAC Stats & ML Journal Club. We'll meet at 9:30 am on the Kavli Third Floor, and on Zoom <https://stanford.zoom.us/j/2038764923> . This week, Dongwoo Chen will be discussing HaloNet, a scheme for generating cosmological mocks with GANs. Should be an interesting discussion!

<https://arxiv.org/pdf/1805.04537.pdf>

Abstract: For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations. Instead of calculating this costly gravitational evolution, we have trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter protohaloes directly from the cosmological initial conditions. Training on halo catalogues from the Peak Patch semianalytic code, we test various CNN architectures and find they generically achieve a Dice coefficient of 92% in only 24 hours of training. We present a simple and fast geometric halo finding algorithm to extract haloes from this powerful pixel-wise binary classifier and find that the predicted catalogues match the mass function and power spectra of the ground truth simulations to within 10%. We investigate the effect of long-range tidal forces on an object-by-object basis and find that the network’s predictions are consistent with the non-linear ellipsoidal collapse equations used explicitly by the Peak Patch algorithm.For modern large-scale structure survey techniques it has become standard practice to test data analysis pipelines on large suites of mock simulations, a task which is currently prohibitively expensive for full N-body simulations. Instead of calculating this costly gravitational evolution, we have trained a three-dimensional deep Convolutional Neural Network (CNN) to identify dark matter protohaloes directly from the cosmological initial conditions. Training on halo catalogues from the Peak Patch semianalytic code, we test various CNN architectures and find they generically achieve a Dice coefficient of 92% in only 24 hours of training. We present a simple and fast geometric halo finding algorithm to extract haloes from this powerful pixel-wise binary classifier and find that the predicted catalogues match the mass function and power spectra of the ground truth simulations to within 10%. We investigate the effect of long-range tidal forces on an object-by-object basis and find that the network’s predictions are consistent with the non-linear ellipsoidal collapse equations used explicitly by the Peak Patch algorithm.