Using 1000 mock realizations matched to the ASPIRE survey, the authors find cosmic variance increases clustering errors by ~3x over Poisson estimates and widens minimum halo mass uncertainties by 1.5-3x for z~6 quasars and emission-line galaxies.
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4 Pith papers cite this work, alongside 330 external citations. Polarity classification is still indexing.
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New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
A new halo occupation model called HOMe reproduces the anisotropic clustering of ELGs and LRGs down to 200 h^{-1} kpc scales by sampling satellites from dark matter particle positions and fitting parameters to two-point statistics.
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.
citing papers explorer
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The Impact of Cosmic Variance and Satellites on JWST Clustering Measurements at Redshift around 6
Using 1000 mock realizations matched to the ASPIRE survey, the authors find cosmic variance increases clustering errors by ~3x over Poisson estimates and widens minimum halo mass uncertainties by 1.5-3x for z~6 quasars and emission-line galaxies.
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Efficient estimators for power spectrum and bispectrum multipole measurements
New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
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ELG$\times$LRG distribution through dark matter halo dynamics
A new halo occupation model called HOMe reproduces the anisotropic clustering of ELGs and LRGs down to 200 h^{-1} kpc scales by sampling satellites from dark matter particle positions and fitting parameters to two-point statistics.
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Constructing a Mock Galaxy Catalog for the All-sky SPECtroscopic Survey of Nearby Galaxies (A-SPEC) Using the Machine-assisted Semi-Simulation Model
A machine learning model trained on IllustrisTNG predicts galaxy baryonic properties from dark matter subhalo features, producing a mock catalog for the A-SPEC survey that matches observed galaxy clustering.