60 research outputs found

    Full forward model of galaxy clustering statistics with simulation lightcones

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    Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r<30hβˆ’1r < 30h^{-1}Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modeling pipeline for any summary statistics using high-fidelity simulation lightcones that accounts for all observational systematics and is appropriate for a wide range of summary statistics. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy--halo connection model using two separate summary statistics: the standard 2PCF and the novel kk-th nearest neighbor (kkNN) statistics, which are sensitive to correlation functions of all orders. We expect that applying this forward model approach to current and upcoming surveys while leveraging a multitude of summary statistics will become a powerful technique in maximally extracting information from the non-linear scales.Comment: comments welcom

    Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform

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    We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the Wavelet Scattering Transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of \textsc{AbacusSummit} simulations combined with a flexible Halo Occupation Distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range 0.46<z<0.570.46<z<0.57. We find that a joint WST + 2-point correlation function likelihood analysis allows us to obtain marginalized 1Οƒ\sigma errors on the Ξ›\LambdaCDM parameters that are tighter by a factor of 2.5βˆ’62.5-6, compared to the 2-point correlation function, and by a factor of 1.4βˆ’2.51.4-2.5 compared to the WST-only results. This corresponds to a competitive 0.9%0.9\%, 2.3%2.3\% and 1%1\% level of determination for parameters Ο‰c\omega_c, Οƒ8\sigma_8 &\& nsn_s, respectively, and also to a 0.7%0.7\% &\& 2.5%2.5 \% constraint on derived parameters h and f(z)Οƒ8(z)f(z)\sigma_8(z), in agreement with the \textit{Planck} 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming Stage-IV spectroscopic observations.Comment: 25 pages, 17 figures, 4 table

    2D k-th nearest neighbor statistics: a highly informative probe of galaxy clustering

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    Beyond standard summary statistics are necessary to summarize the rich information on non-linear scales in the era of precision galaxy clustering measurements. For the first time, we introduce the 2D k-th nearest neighbor (kNN) statistics as a summary statistic for discrete galaxy fields. This is a direct generalization of the standard 1D kNN by disentangling the projected galaxy distribution from the redshift-space distortion signature along the line-of-sight. We further introduce two different flavors of 2D kkNNs that trace different aspects of the galaxy field: the standard flavor which tabulates the distances between galaxies and random query points, and a ''DD'' flavor that tabulates the distances between galaxies and galaxies. We showcase the 2D kNNs' strong constraining power both through theoretical arguments and by testing on realistic galaxy mocks. Theoretically, we show that 2D kNNs are computationally efficient and directly generate other statistics such as the popular 2-point correlation function, voids probability function, and counts-in-cell statistics. In a more practical test, we apply the 2D kNN statistics to simulated galaxy mocks that fold in a large range of observational realism and recover parameters of the underlying extended halo occupation distribution (HOD) model that includes velocity bias and galaxy assembly bias. We find unbiased and significantly tighter constraints on all aspects of the HOD model with the 2D kNNs, both compared to the standard 1D kNN, and the classical redshift-space 2-point correlation functions.Comment: Submitted to MNRAS, comments welcom
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