CosmoSIS: Modular cosmological parameter estimation

Zuntz, Joe; Paterno, M.; Jennings, Elise; Rudd, Douglas H.; Manzotti, Alessandro; Dodelson, Scott; Bridle, Sarah; Sehrish, Saba; Kowalkowski, Jim

doi:10.1016/j.ascom.2015.05.005

Cited by 359 publications

(322 citation statements)

References 39 publications

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“…Again, all assumptions and approximations mentioned here have been shown to be inconsequential in [20,21]. To model cross-correlation between redshift bins we simply change n Throughout this paper, we use the COSMOSIS framework [23] to compute correlation functions, and to infer cosmological parameters. The evolution of linear density fluctuations is obtained using the CAMB module [24] and then converted to a nonlinear matter power spectrum P NL ðkÞ using the updated Halofit recipe [25].…”

Section: Theorymentioning

confidence: 99%

Dark Energy Survey year 1 results: Galaxy clustering for combined probes

Elvin-Poole¹,

Crocce²,

Ross³

et al. 2018

Phys. Rev. D

Self Cite

159

235

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We measure the clustering of DES year 1 galaxies that are intended to be combined with weak lensing samples in order to produce precise cosmological constraints from the joint analysis of large-scale structure and lensing correlations. Two-point correlation functions are measured for a sample of 6.6 × 10 5 luminous red galaxies selected using the REDMAGIC algorithm over an area of 1321 square degrees, in the redshift range 0.15 < z < 0.9, split into five tomographic redshift bins. The sample has a mean redshift uncertainty of σ z =ð1 þ zÞ ¼ 0.017. We quantify and correct spurious correlations induced by spatially variable survey properties, testing their impact on the clustering measurements and covariance. We demonstrate the sample's robustness by testing for stellar contamination, for potential biases that could arise from the systematic correction, and for the consistency between the two-point auto-and cross-correlation functions. We show that the corrections we apply have a significant impact on the resultant measurement of cosmological parameters, but that the results are robust against arbitrary choices in the correction method. We find the linear galaxy bias in each redshift bin in a fiducial cosmology to be bðσ 8 =0.81Þj z¼0.24 ¼ 1.40 AE 0.07, bðσ 8 =0.81Þj z¼0.38 ¼ 1.60 AE 0.05, bðσ 8 =0.81Þj z¼0.53 ¼ 1.60 AE 0.04 for galaxies with luminosities L=L Ã > 0.5, bðσ 8 =0.81Þj z¼0.68 ¼ 1.93 AE 0.04 for L=L Ã > 1 and bðσ 8 =0.81Þj z¼0.83 ¼ 1.98 AE 0.07 for L=L Ã > 1.5, broadly consistent with expectations for the redshift and luminosity dependence of the bias of red galaxies. We show these measurements to be consistent with the linear bias obtained from tangential shear measurements.

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Section: Theorymentioning

confidence: 99%

Dark Energy Survey year 1 results: Galaxy clustering for combined probes

Elvin-Poole¹,

Crocce²,

Ross³

et al. 2018

Phys. Rev. D

Self Cite

159

235

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“…Key results for this paper have been calculated with two separate pipelines: the CosmoSIS 8 [120] and CosmoLike [34] frameworks. The constraints from these independent pipelines agree extremely well and thus are not shown separately.…”

Section: Fiducial Cosmological Constraintsmentioning

confidence: 99%

Cosmology from cosmic shear with Dark Energy Survey Science Verification data

Abbott¹,

Abdalla²,

Allam³

et al. 2016

Phys. Rev. D

Self Cite

154

113

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, UKWe present the first constraints on cosmology from the Dark Energy Survey (DES), using weak lensing measurements from the preliminary Science Verification (SV) data. We use 139 square degrees of SV data, which is less than 3% of the full DES survey area. Using cosmic shear 2-point measurements over three redshift bins we find σ8(Ωm/0.3) 0.5 = 0.81 ± 0.06 (68% confidence), after marginalising over 7 systematics parameters and 3 other cosmological parameters. We examine the robustness of our results to the choice of data vector and systematics assumed, and find them to be stable. About 20% of our error bar comes from marginalising over shear and photometric redshift calibration uncertainties. The current state-of-the-art cosmic shear measurements from CFHTLenS are mildly discrepant with the cosmological constraints from Planck CMB data; our results are consistent with both datasets. Our uncertainties are ∼30% larger than those from CFHTLenS when we carry out a comparable analysis of the two datasets, which we attribute largely to the lower number density of our shear catalogue. We investigate constraints on dark energy and find that, with this small fraction of the full survey, the DES SV constraints make negligible impact on the Planck constraints. The moderate disagreement between the CFHTLenS and Planck values of σ8(Ωm/0.3) 0.5 is present regardless of the value of w.

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“…The power spectrum is generated using CAMB [49] and the nonlinear part is generated using HALOFIT [42], produced as part of the Cosmosis [14] pipeline, each run with the default setting given in the demo1 tutorial in Cosmosis.…”

Section: Appendix E: Modeling Choicesmentioning

confidence: 99%

Preparing for the cosmic shear data flood: Optimal data extraction and simulation requirements for stage IV dark energy experiments

2018

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Upcoming photometric lensing surveys will considerably tighten constraints on the neutrino mass and the dark energy equation of state. Nevertheless it remains an open question of how to optimally extract the information and how well the matter power spectrum must be known to obtain unbiased cosmological parameter estimates. By performing a principal component analysis (PCA), we quantify the sensitivity of 3D cosmic shear and tomography with different binning strategies to different regions of the lensing kernel and matter power spectrum, and hence the background geometry and growth of structure in the Universe. We find that a large number of equally spaced tomographic bins in redshift can extract nearly all the cosmological information without the additional computational expense of 3D cosmic shear. Meanwhile a large fraction of the information comes from small poorly understood scales in the matter power spectrum, that can lead to biases on measurements of cosmological parameters. In light of this, we define and compute a cosmology-independent measure of the bias due to imperfect knowledge of the power spectrum. For a Euclid-like survey, we find that the power spectrum must be known to an accuracy of less than 1% on scales with k ≤ 7h Mpc −1 . This requirement is not absolute since the bias depends on the magnitude of modeling errors, where they occur in k-z space, and the correlation between them, all of which are specific to any particular model. We therefore compute the bias in several of the most likely modeling scenarios and introduce a general formalism and public code, RequiSim, to compute the expected bias from any nonlinear model.

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CosmoSIS: Modular cosmological parameter estimation

Cited by 359 publications

References 39 publications

Dark Energy Survey year 1 results: Galaxy clustering for combined probes

Dark Energy Survey year 1 results: Galaxy clustering for combined probes

Cosmology from cosmic shear with Dark Energy Survey Science Verification data

Preparing for the cosmic shear data flood: Optimal data extraction and simulation requirements for stage IV dark energy experiments

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