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Abstract We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ∼ 60h−1Mpc. Although our simulations are based onwCDM with general relativity (GR), we include a scaling parameter of the halo velocity field,γf, defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we findfσ8(z= 0.25) = 0.413 ± 0.031,fσ8(z= 0.4) = 0.470 ± 0.026, andfσ8(z= 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1.9σ, 0.3σ, and 3.4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of . We note, however, that the BOSS data is well fit assuming GR, i.e.,γf= 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear scales, but near-future data and modeling will enhance our understanding of the galaxy–halo connection, and provide a strong test of new physics beyond the standard model.
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Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations
Abstract We present a proof-of-concept simulation-based inference on Ωmandσ8from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-picola, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network,Minkowski-PointNet. We also add relatively more accurate Gadgetmocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find Ωm= 0.339 ± 0.056 andσ8= 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadgetmocks yields a tighter prediction of Ωm= 0.282 ± 0.014 andσ8= 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.
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- Award ID(s):
- 2406729
- PAR ID:
- 10612803
- Publisher / Repository:
- The Astrophysical Journal
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 975
- Issue:
- 1
- ISSN:
- 0004-637X
- Page Range / eLocation ID:
- 38
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3847/1538-4357/ad73d4
