Non-parametric Lagrangian biasing from the insights of neural nets

Abstract

We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo field in the AbacusSummit simulations at z=0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2-5% underestimation of the large-scale power and can cause the neural net to overfit. We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space. Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales. Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.

submitted to JCAP

References in corpus (12)

• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Clustering of dark matter tracers: generalizing bias for the coming era of precision LSS
• Gravity and Large-Scale Non-local Bias
• AbacusSummit: A Massive Set of High-Accuracy, High-Resolution $N$-Body Simulations
• Understanding higher-order nonlocal halo bias at large scales by combining the power spectrum with the bispectrum
• Modeling scale-dependent bias on the baryonic acoustic scale with the statistics of peaks of Gaussian random fields
• The $\text{Abacus}$ Cosmological $N$-body Code
• The Gaussian streaming model and Lagrangian effective field theory
• \textsc{CompaSO}: A new halo finder for competitive assignment to spherical overdensities
• Simulations and symmetries
• Galaxy bias from forward models: linear and second-order bias of IllustrisTNG galaxies
• A fully Lagrangian, non-parametric bias model for dark-matter halos

Cited by in corpus (1)

• Gaussian Lagrangian Galaxy Bias