A Tale of Two Fields: Neural Network-Enhanced non-Gaussianity Search with Halos

Understand

It was recently shown that neural networks can be combined with the analytic method of scale-dependent bias to obtain a measurement of local primordial non-Gaussianity, which is optimal in the squeezed limit that dominates the signal-to-noise.

• The method is robust to non-linear physics, but also inherits the statistical precision offered by neural networks applied to very non-linear scales.
• In prior work, we assumed that the neural network has access to the full matter distribution.
• In this work, we apply our method to halos.