Pith. sign in

REVIEW 1 cited by

Mock galaxy catalogs using the quick particle mesh method

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1309.5532 v2 pith:PIFXU22U submitted 2013-09-21 astro-ph.CO

classification astro-ph.CO
keywords mockcatalogsanalysisdensitymeshmethodparticlefield
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Sophisticated analysis of modern large-scale structure surveys requires mock catalogs. Mock catalogs are used to optimize survey design, test reduction and analysis pipelines, make theoretical predictions for basic observables and propagate errors through complex analysis chains. We present a new method, which we call "quick particle mesh", for generating many large-volume, approximate mock catalogs at low computational cost. The method is based on using rapid, low-resolution particle mesh simulations that accurately reproduce the large-scale dark matter density field. Particles are sampled from the density field based on their local density such that they have N-point statistics nearly equivalent to the halos resolved in high-resolution simulations, creating a set of mock halos that can be populated using halo occupation methods to create galaxy mocks for a variety of possible target classes.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DESI DR2 Reference Mocks: Clustering results from UCHUU ELGs and QSOs

    astro-ph.CO 2026-06 unverdicted novelty 4.0 of 10

    Mock catalogs for DESI DR2 ELGs and QSOs are constructed via modified subhalo abundance matching on the Uchuu simulation to reproduce observed number density and clustering statistics.

Pith tools