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Title: 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 theAbacusSummitsimulations atz= 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.  more » « less
Award ID(s):
2019786
PAR ID:
10505454
Author(s) / Creator(s):
; ;
Publisher / Repository:
IOP
Date Published:
Journal Name:
Journal of Cosmology and Astroparticle Physics
Volume:
2023
Issue:
05
ISSN:
1475-7516
Page Range / eLocation ID:
040
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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