Enhancing cosmological constraints with nonlinear tanh transformations of Hermite-Gaussian Derivative fields

TL;DR

This paper introduces a robust multi-scale derivative and nonlinear transformation framework using Hermite-Gaussian filters and tanh functions to improve cosmological parameter constraints from large-scale structure data.

Contribution

The authors develop a novel two-step method combining stable multi-scale derivatives with tanh nonlinearities, enhancing cosmological information extraction from simulations.

Findings

Multi-scale first-order spectra improve parameter constraints by 1.2-3.0 times.

Multi-order spectra at fixed scales yield 1.3-2.9 times gains.

Combining all methods achieves 2.0-5.3 times improvement.

Abstract

A key goal in large-scale structure analysis is to extract multi-scale information to improve cosmological parameter constraints. In particular, higher-order derivative fields are especially valuable as they capture the geometric and topological information of the cosmic web that is highly sensitive to cosmological parameters. Traditional derivative-based methods, such as finite-difference or Fourier approaches, suffer from noise amplification at small scales and cannot stably capture multi-scale features. We present a robust two-step framework: first, stable multi-scale arbitrary-order derivatives are obtained via Hermite-Gaussian convolutional filters that suppress small-scale noise; second, a tanh nonlinear transformation compresses extreme density contrasts and enhances the visibility of cosmic web structures. Using the Quijote simulations, we show that combining multi-scale first-order spectra yields improvements of 1.2-3.0 times across all seven cosmological parameters, while multi-order spectra at a fixed scale provide 1.3-2.9 times gains. The most comprehensive combination achieves nominal gains of 2.0-5.3 times. Our method offers a robust approach to extracting additional cosmological information for future surveys.