Search Results for author: Hume A. Feldman

Found 4 papers, 0 papers with code

Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks

no code implementations8 Oct 2020 Yuyu Wang, Nesar Ramachandra, Edgar M. Salazar-Canizales, Hume A. Feldman, Richard Watkins, Klaus Dolag

The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys.

Cosmology and Nongalactic Astrophysics

PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks

no code implementations7 Dec 2013 Shankar Agarwal, Filipe B. Abdalla, Hume A. Feldman, Ofer Lahav, Shaun A. Thomas

The overall precision of PkANN is set by the accuracy of our N-body simulations, at 5 per cent level for cosmological models with $\sum m_\nu<0. 5$ eV for all redshifts $z\leq2$.

Cosmology and Nongalactic Astrophysics

PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks

no code implementations8 Mar 2012 Shankar Agarwal, Filipe B. Abdalla, Hume A. Feldman, Ofer Lahav, Shaun A. Thomas

We show that an optimally trained artificial neural network (ANN), when presented with a set of cosmological parameters (Omega_m h^2, Omega_b h^2, n_s, w_0, sigma_8, m_nu and redshift z), can provide a worst-case error <=1 per cent (for z<=2) fit to the non-linear matter power spectrum deduced through N-body simulations, for modes up to k<=0. 7 h/Mpc.

Cosmology and Nongalactic Astrophysics

Power Spectrum Analysis of Three-Dimensional Redshift Surveys

no code implementations26 Apr 1993 Hume A. Feldman, Nick Kaiser, John A. Peacock

We apply likelihood analysis using the CDM spectrum with $\Omega h$ as a free parameter as a phenomenological family of models; we find the best fitting parameters in redshift space and transform the results to real space.

astro-ph

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