A CNN for cosmological parameter estimation from large-scale structure relies on both Gaussian and non-Gaussian information, emphasizing scales at the linear-to-nonlinear transition.
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Emulation of binned modified gravity power spectra to <1% accuracy enables MCMC forecasts that constrain μ and η via LSST large-scale structure combined with CMB lensing, with best sensitivity along the lensing combination Σ.
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Interpretability of deep-learning methods applied to large-scale structure surveys
A CNN for cosmological parameter estimation from large-scale structure relies on both Gaussian and non-Gaussian information, emphasizing scales at the linear-to-nonlinear transition.
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Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity
Emulation of binned modified gravity power spectra to <1% accuracy enables MCMC forecasts that constrain μ and η via LSST large-scale structure combined with CMB lensing, with best sensitivity along the lensing combination Σ.