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The effective field theory likelihood for the density field of biased tracers allows for cosmology inference from the clustering of galaxies that consistently uses all available information at a given order in perturbation theory.
1906
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1906
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1907
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G. Cabass and F. Schmidt, The EFT Likelihood for Large-Scale Structure
1909
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W. H. Press and P. Schechter, Formation of Galaxies and Clusters of Galaxies by Self-Similar Gravitational Condensation
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K. B. Fisher, O. Lahav, Y. Hoffman, D. Lynden-Bell and S. Zaroubi, Wiener reconstruction of density, velocity and potential fields from all-sky galaxy redshift surveys
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G. Cabass and F. Schmidt, The Likelihood for LSS: Stochasticity of Bias Coefficients at All Orders
2004
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S. P. D. Gill, A. Knebe and B. K. Gibson, The evolution of substructure - I. A new identification method
2004
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V. Springel, The cosmological simulation code GADGET-2
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D. J. Eisenstein, H.-J. Seo, E. Sirko and D. N. Spergel, Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
2007
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2007
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G. Cabass, The EFT Likelihood for Large-Scale Structure in Redshift Space
2007
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2008
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2009
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2009
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P. S. Behroozi, R. H. Wechsler and H.-Y. Wu, The rockstar phase-space temporal halo finder and the velocity offsets of cluster cores
2012
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2013
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2014
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2014
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2009
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2010
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2010
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J. Jasche and F. S. Kitaura, Fast Hamiltonian sampling for large-scale structure inference
2010
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2010
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2010
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G. Guennebaud, B. Jacob et al., “Eigen v3.” http://eigen.tuxfamily.org, 2010
2010
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2011
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2015
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T. Matsubara, Recursive solutions of Lagrangian perturbation theory
2015
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M. Mirbabayi, F. Schmidt and M. Zaldarriaga, Biased tracers and time evolution
2015
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2016
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2017
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2017
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2017
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V. Desjacques, D. Jeong and F. Schmidt, Large-scale galaxy bias
2018
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2018
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2018
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V. Desjacques, D. Jeong and F. Schmidt, The galaxy power spectrum and bispectrum in redshift space
2018
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2018
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2019
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2019
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