November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Fast realistic, differentiable, mock halo generation for wide-field galaxy surveys

Nov 29, 2023, 5:06 PM
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Flash talk Paris Contributed talks


Simon Ding (Institut d'Astrophysique de Paris (IAP))


Accurately describing the relation between the dark matter over-density and the observable galaxy field is one of the significant challenges to analyzing cosmic structures with next-generation galaxy surveys. Current galaxy bias models are either inaccurate or computationally too expensive to be used for efficient inference of small-scale information.
In this talk, I will present a hybrid machine learning approach called the Neural Physical Engine (NPE) that addresses this problem. The network architecture, first developed and tested by Charnock et al. (2020), exploits physical information of the galaxy bias problem and is suitable for zero-shot learning within field-level inference approaches.
Furthermore, the model can efficiently generate mock halo catalogues on the scales of wide-field surveys such as Euclid. Finally, I will also show that those generated mocks are consistent with full phase-space halo finders, including the 2-point correlation function.

Primary author

Simon Ding (Institut d'Astrophysique de Paris (IAP))

Presentation materials