Results 1 to 10 of about 60,808,388 (219)
Learning from topology: cosmological parameter estimation from the large-scale structure
Machine Learning: Science and TechnologyThe topology of the large-scale structure of the Universe contains valuable information on the underlying cosmological parameters. While persistent homology can extract this topological information, the optimal method for parameter estimation from the ...
Jacky H T Yip +2 more
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Cosmological parameter estimation from large-scale structure deep learning [PDF]
Science China Physics, Mechanics & Astronomy, 2020 17 pages, 10 figures, 1 ...
Pan, Shuyang +6 more
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Cosmological parameters from large scale structure - geometric versus shape information [PDF]
Journal of Cosmology and Astroparticle Physics, 2010 The matter power spectrum as derived from large scale structure (LSS) surveys contains two important and distinct pieces of information: an overall smooth shape and the imprint of baryon acoustic oscillations (BAO). We investigate the separate impact of these two types of information on cosmological parameter estimation, and show that for the simplest ...
Hamann, Jan +4 more
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BEST-FIT COSMOLOGICAL PARAMETERS FROM OBSERVATIONS OF THE LARGE SCALE STRUCTURE OF THE UNIVERSE
Odessa Astronomical Publications, 2017 The possibility to determine cosmological parameters on the basis of a wide set of observational data including the Abell-ACO cluster power spectrum and mass function, peculiar velocities of galaxies, the distribution of Ly-α clouds and CMB temperature ...
B. Novosyadlyj +4 more
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CoLFI: Cosmological Likelihood-free Inference with Neural Density Estimators
The Astrophysical Journal Supplement Series, 2023 In previous works, we proposed to estimate cosmological parameters with an artificial neural network (ANN) and a mixture density network (MDN). In this work, we propose an improved method called a mixture neural network (MNN) to achieve parameter ...
Guo-Jian Wang +5 more
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The Astrophysical Journal, 2023 Primordial non-Gaussianity (PNG) is one of the most powerful probes of the early universe, and measurements of the large-scale structure of the universe have the potential to transform our understanding of this area. However, relating measurements of the
William R Coulton +8 more
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Robust Field-level Inference of Cosmological Parameters with Dark Matter Halos
The Astrophysical Journal, 2023 We train graph neural networks on halo catalogs from Gadget N -body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogs contain ≲5000 halos with masses ≳10 ^10 h ^−1 M _⊙ in a periodic volume of ${(25\,{h}
Helen Shao +15 more
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Cosmological Constraints from the BOSS DR12 Void Size Function
The Astrophysical Journal, 2023 We present the first cosmological constraints derived from the analysis of the void size function. This work relies on the final Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 12 (DR12) data set, a large spectroscopic galaxy catalog, ideal ...
Sofia Contarini +5 more
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The Astrophysical Journal, 2023 We study primordial non-Gaussian signatures in the redshift-space halo field on nonlinear scales, using a quasi-maximum likelihood estimator based on optimally compressed power spectrum and modal bispectrum statistics. We train and validate the estimator
Gabriel Jung +8 more
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Learning Neutrino Effects in Cosmology with Convolutional Neural Network
The Astrophysical Journal, 2023 Measuring the sum of the three active neutrino masses, M _ν , is one of the most important challenges in modern cosmology. Massive neutrinos imprint characteristic signatures on several cosmological observables, in particular, on the large-scale ...
Elena Giusarma +5 more
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