Ridged Lagrangian Perturbation Theory (RLPT)

TL;DR

RLPT is a novel two-step scheme that enhances large-scale structure modeling by combining LPT with a ridging update, improving small-scale clustering accuracy and enabling controlled subgrid tracer relocation.

Contribution

Introduces Ridged Lagrangian Perturbation Theory (RLPT), a modular method that improves nonlinear structure modeling by adding a computationally inexpensive ridging step after standard LPT.

Findings

RLPT systematically improves nonlinear power spectra.

RLPT enhances field-level agreement with N-body simulations.

Ridging enables controlled subgrid clustering adjustments.

Abstract

Galaxy surveys demand fast large-scale structure forward models that preserve large-scale phases while providing realistic nonlinear morphology at fixed force resolution. Single-step Lagrangian Perturbation Theory (LPT) solvers are efficient, but they typically yield overly diffuse filaments and knots and underpredict small-scale clustering. We introduce Ridged Lagrangian Perturbation Theory (RLPT), a modular two-step scheme: a standard long-range LPT/ALPT transport is followed by a single post-processing Eulerian ridging update that reconstructs a short-range, curl-free displacement from the realised density field through a smooth scale separation and a Poisson inversion. This explicit completion layer is inexpensive, preserves the large-scale solution, and provides a small set of transparent parameters to tune the short-range response. We test RLPT against particle-mesh and $N$-body references and find that one additional ridging step systematically improves both nonlinear power and field-level agreement relative to 2LPT/ALPT baselines. Finally, we demonstrate that ridging can be repurposed as a deterministic subgrid relocation model: even when the underlying dark-matter field is only ``good enough'' on the mesh, ridging enables controlled tuning of tracer clustering beyond the nominal resolution, which is particularly relevant for mock-galaxy production and observational systematics sensitive to close pairs.