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Sökning: WFRF:(Pardede S.)

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1.
  • Pezzotta, A., et al. (författare)
  • Euclid preparation XLI. Galaxy power spectrum modelling in real space
  • 2024
  • Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 687
  • Tidskriftsartikel (refereegranskat)abstract
    • We investigate the accuracy of the perturbative galaxy bias expansion in view of the forthcoming analysis of the Euclid spectroscopic galaxy samples. We compare the performance of a Eulerian galaxy bias expansion using state-of-the-art prescriptions from the effective field theory of large-scale structure (EFTofLSS) with a hybrid approach based on Lagrangian perturbation theory and high-resolution simulations. These models are benchmarked against comoving snapshots of the flagship I N-body simulation at z = (0.9, 1.2, 1.5, 1.8), which have been populated with H alpha galaxies leading to catalogues of millions of objects within a volume of about 58 h(-3) Gpc(3). Our analysis suggests that both models can be used to provide a robust inference of the parameters (h, omega c) in the redshift range under consideration, with comparable constraining power. We additionally determine the range of validity of the EFTofLSS model in terms of scale cuts and model degrees of freedom. From these tests, it emerges that the standard third-order Eulerian bias expansion - which includes local and non-local bias parameters, a matter counter term, and a correction to the shot-noise contribution - can accurately describe the full shape of the real-space galaxy power spectrum up to the maximum wavenumber of k(max) = 0.45 h Mpc(-1), and with a measurement precision of well below the percentage level. Fixing either of the tidal bias parameters to physically motivated relations still leads to unbiased cosmological constraints, and helps in reducing the severity of projection effects due to the large dimensionality of the model. We finally show how we repeated our analysis assuming a volume that matches the expected footprint of Euclid, but without considering observational effects, such as purity and completeness, showing that we can get constraints on the combination (h, omega c) that are consistent with the fiducial values to better than the 68% confidence interval over this range of scales and redshifts.
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2.
  • Dornelas, M., et al. (författare)
  • BioTIME: A database of biodiversity time series for the Anthropocene
  • 2018
  • Ingår i: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 27:7, s. 760-786
  • Tidskriftsartikel (refereegranskat)abstract
    • Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community-led open-source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km(2) (158 cm(2)) to 100 km(2) (1,000,000,000,000 cm(2)). Time period and grainBio: TIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.
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3.
  • Di Fatta, G., et al. (författare)
  • Preface
  • 2011
  • Ingår i: IEEE International Conference on Data Mining. Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). - 1550-4786. ; , s. xlviii-xlvix
  • Tidskriftsartikel (refereegranskat)
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