Debating the potential of machine learning in astronomical surveys

Accelerating the modeling of HI on cosmological scales via Deep Learning
Robert Feldmann  1@  , Mauro Bernardini  1  , Joachim Stadel  1  , Lucio Mayer  1  , James Bullock  2  , Daniel Anglés-Alcázar  3  , Michael Boylan-Kolchin  4  
1 : University of Zürich [Zürich]
2 : University of California [Irvine]
3 : University of Connecticut
4 : University of Texas at Austin  (UT Austin)

Hydrodynamical simulations offer a promising framework to study the formation and distribution of atomic hydrogen in the Universe but they often pose significant computational obstacles. We present a new method based on Deep Convolutional Neural Networks to efficiently predict atomic hydrogen maps from dark matter fields. After being trained on a combination of zoom-in simulations and small cosmological boxes, this approach is able to reproduce the gas and HI power spectra in the simulations over a broad range in scales down to galaxy scales (~10 kpc) with a ~10% accuracy and at neglible computational cost. This methodology is subsequently applied to predict high-resolution HI maps for larger cosmological volumes based on dark-matter-only simulations. Applications, advantages, and drawbacks of this approach are briefly discussed.

 

Poster: in PDF

Video: https://youtu.be/wDK9ZWHU4nE


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