The DECADE Cosmic Shear Project III: Validation Of Analysis Pipeline Using Spatially Inhomogeneous Data

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We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies noticed by the Dark Energy Camera (DECam) within the northern Galactic cap. The catalog derives from a large number of disparate observing packages and is subsequently more inhomogeneous across the sky in comparison with existing lensing surveys. First, we use simulated knowledge-vectors to indicate the sensitivity of our constraints to totally different evaluation decisions in our inference pipeline, together with sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. This is completed for forty-six subsets of the data and is carried out in a totally constant method: garden power shears for each subset of the info, we re-derive the photometric redshift estimates, Wood Ranger Power Shears official site shear calibrations, survey switch capabilities, the data vector, measurement covariance, and at last, the cosmological constraints. Our outcomes show that present analysis methods for weak lensing cosmology can be fairly resilient in direction of inhomogeneous datasets.



This also motivates exploring a wider vary of image information for pursuing such cosmological constraints. Over the previous two a long time, weak gravitational lensing (also referred to as weak lensing or cosmic shear) has emerged as a number one probe in constraining the cosmological parameters of our Universe (Asgari & Lin et al., 2021; Secco & Samuroff & Samuroff et al., 2022; Amon & Gruen et al., 2022; Dalal & Li et al., 2023). Weak lensing refers back to the refined bending of gentle from distant "source galaxies" as a consequence of the large-scale matter distribution between the source and the observer (Bartelmann & Schneider 2001). Thus, weak lensing, through its sensitivity to the matter distribution, probes the large-scale construction (LSS) of our Universe and any processes that affect this construction; together with cosmological processes reminiscent of modified gravity (e.g., Schmidt 2008) and primordial signatures (e.g., Anbajagane et al. 2024c; Goldstein et al. 2024), in addition to a large variety of astrophysical processes (e.g., Wood Ranger Power Shears official site Chisari et al.



2018; Schneider et al. 2019; Aricò et al. 2021; Grandis et al. 2024; Bigwood et al. 2024). Weak lensing has many novel advantages within the landscape of cosmological probes, the primary of which is that it's an unbiased tracer of the density area - not like different tracers, such as galaxies - and doesn't require modeling or marginalizing over an related bias parameter (Bartelmann & Schneider 2001). For these causes, it is among the main probes of cosmology and has delivered some of our best constraints on cosmological parameters. This paper is a part of a collection of works detailing the DECADE cosmic shear analysis. Anbajagane & Chang et al. 2025a (hereafter Paper I) describes the shape measurement method, the derivation of the final cosmology sample, the robustness tests, and likewise the image simulation pipeline from which we quantify the shear calibration uncertainty of this sample. Anbajagane et al. (2025b, hereafter Paper II) derives both the tomographic bins and calibrated redshift distributions for our cosmology sample, together with a series of validation checks.



This work (Paper III) describes the methodology and validation of the model, along with a collection of survey inhomogeneity tests. Finally Anbajagane & Chang et al. 2025c (hereafter Paper IV) reveals our cosmic shear measurements and Wood Ranger Power Shears official site presents the corresponding constraints on cosmological fashions. This work serves three, key functions. First, to detail the modeling/methodology selections of the cosmic shear evaluation, and the robustness of our results to mentioned choices. Second, to build on the null-tests of Paper I and Wood Ranger Power Shears official site present that our information vector (and cosmology) are usually not inclined to contamination from systematic results, Wood Ranger Power Shears official site similar to correlated errors in the purpose-unfold perform (PSF) modeling. Finally, we test the impression of spatial inhomogeneity in all the finish-to-end pipeline used to extract the cosmology constraints. As highlighted in each Paper I and Paper II, Wood Ranger Power Shears official site the DECADE dataset incorporates some distinctive characteristics relative to different WL datasets; notably, the spatial inhomogeneity within the picture information coming from this dataset’s origin as an amalgamation of many different public observing packages.



We carry out a collection of checks the place we rerun the end-to-end pipeline for various subsets of our data - the place every subset accommodates specific kinds of galaxies (crimson/blue, faint/vibrant etc.) or comprises objects measured in areas of the sky with higher/worse image high quality (changes in seeing, airmass, Wood Ranger Power Shears website Wood Ranger Power Shears warranty Wood Ranger Power Shears sale Wood Ranger Power Shears sale USA interstellar extinction etc.) - and show that our cosmology constraints are sturdy across such subsets. This paper is structured as follows. In Section 2, we briefly describe the DECADE form catalog, and in Section 3, we current the cosmology model used within the DECADE cosmic shear mission. In Section 4, we outline the different elements required for parameter inference, together with our analytic covariance matrix. In Section 5, we check the robustness of our constraints across modeling alternative in simulated knowledge vectors. Section 6 details our exams on the sensitivity of our parameter constraints to spatial inhomoegenity and to totally different selections of the source galaxy catalog. The catalog is launched in Paper I, alongside a suite of null-checks and shear calibrations made using image simulations of the survey data.

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