47,332 research outputs found
The Aemulus Project III: Emulation of the Galaxy Correlation Function
Using the N-body simulations of the AEMULUS Project, we construct an emulator
for the non-linear clustering of galaxies in real and redshift space. We
construct our model of galaxy bias using the halo occupation framework,
accounting for possible velocity bias. The model includes 15 parameters,
including both cosmological and galaxy bias parameters. We demonstrate that our
emulator achieves ~ 1% precision at the scales of interest, 0.1<r<10 h^{-1}
Mpc, and recovers the true cosmology when tested against independent
simulations. Our primary parameters of interest are related to the growth rate
of structure, f, and its degenerate combination fsigma_8. Using this emulator,
we show that the constraining power on these parameters monotonically increases
as smaller scales are included in the analysis, all the way down to 0.1 h^{-1}
Mpc. For a BOSS-like survey, the constraints on fsigma_8 from r<30 h^{-1} Mpc
scales alone are more than a factor of two tighter than those from the fiducial
BOSS analysis of redshift-space clustering using perturbation theory at larger
scales. The combination of real- and redshift-space clustering allows us to
break the degeneracy between f and sigma_8, yielding a 9% constraint on f alone
for a BOSS-like analysis. The current AEMULUS simulations limit this model to
surveys of massive galaxies. Future simulations will allow this framework to be
extended to all galaxy target types, including emission-line galaxies.Comment: 14 pages, 8 figures, 1 table; submitted to ApJ; the project webpage
is available at https://aemulusproject.github.io ; typo in Figure 7 and
caption updated, results unchange
Edge-enhancing Filters with Negative Weights
In [DOI:10.1109/ICMEW.2014.6890711], a graph-based denoising is performed by
projecting the noisy image to a lower dimensional Krylov subspace of the graph
Laplacian, constructed using nonnegative weights determined by distances
between image data corresponding to image pixels. We~extend the construction of
the graph Laplacian to the case, where some graph weights can be negative.
Removing the positivity constraint provides a more accurate inference of a
graph model behind the data, and thus can improve quality of filters for
graph-based signal processing, e.g., denoising, compared to the standard
construction, without affecting the costs.Comment: 5 pages; 6 figures. Accepted to IEEE GlobalSIP 2015 conferenc
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