329,553 research outputs found

### Non-negative matrix factorization for self-calibration of photometric redshift scatter in weak lensing surveys

Photo-z error is one of the major sources of systematics degrading the
accuracy of weak lensing cosmological inferences. Zhang et al. (2010) proposed
a self-calibration method combining galaxy-galaxy correlations and galaxy-shear
correlations between different photo-z bins. Fisher matrix analysis shows that
it can determine the rate of photo-z outliers at a level of 0.01-1% merely
using photometric data and do not rely on any prior knowledge. In this paper,
we develop a new algorithm to implement this method by solving a constrained
nonlinear optimization problem arising in the self-calibration process. Based
on the techniques of fixed-point iteration and non-negative matrix
factorization, the proposed algorithm can efficiently and robustly reconstruct
the scattering probabilities between the true-z and photo-z bins. The algorithm
has been tested extensively by applying it to mock data from simulated stage IV
weak lensing projects. We find that the algorithm provides a successful
recovery of the scatter rates at the level of 0.01-1%, and the true mean
redshifts of photo-z bins at the level of 0.001, which may satisfy the
requirements in future lensing surveys.Comment: 12 pages, 6 figures. Accepted for publication in ApJ. Updated to
match the published versio

### Weak lensing power spectrum reconstruction by counting galaxies.-- I: the ABS method

We propose an Analytical method of Blind Separation (ABS) of cosmic
magnification from the intrinsic fluctuations of galaxy number density in the
observed galaxy number density distribution. The ABS method utilizes the
different dependences of the signal (cosmic magnification) and contamination
(galaxy intrinsic clustering) on galaxy flux, to separate the two. It works
directly on the measured cross galaxy angular power spectra between different
flux bins. It determines/reconstructs the lensing power spectrum analytically,
without assumptions of galaxy intrinsic clustering and cosmology. It is
unbiased in the limit of infinite number of galaxies. In reality the lensing
reconstruction accuracy depends on survey configurations, galaxy biases, and
other complexities, due to finite number of galaxies and the resulting shot
noise fluctuations in the cross galaxy power spectra. We estimate its
performance (systematic and statistical errors) in various cases. We find that,
stage IV dark energy surveys such as SKA and LSST are capable of reconstructing
the lensing power spectrum at $z\simeq 1$ and \ell\la 5000 accurately. This
lensing reconstruction only requires counting galaxies, and is therefore highly
complementary to the cosmic shear measurement by the same surveys.Comment: v1: 13 pages, 10 figures. v2: minor revisions. ApJ in pres

### Kriging Interpolating Cosmic Velocity Field

[abridged] Volume-weighted statistics of large scale peculiar velocity is
preferred by peculiar velocity cosmology, since it is free of uncertainties of
galaxy density bias entangled in mass-weighted statistics. However, measuring
the volume-weighted velocity statistics from galaxy (halo/simulation particle)
velocity data is challenging. For the first time, we apply the Kriging
interpolation to obtain the volume-weighted velocity field. Kriging is a
minimum variance estimator. It predicts the most likely velocity for each place
based on the velocity at other places. We test the performance of Kriging
quantified by the E-mode velocity power spectrum from simulations. Dependences
on the variogram prior used in Kriging, the number $n_k$ of the nearby
particles to interpolate and the density $n_P$ of the observed sample are
investigated. First, we find that Kriging induces $1\%$ and $3\%$ systematics
at $k\sim 0.1h{\rm Mpc}^{-1}$ when $n_P\sim 6\times 10^{-2} ({\rm Mpc}/h)^{-3}$
and $n_P\sim 6\times 10^{-3} ({\rm Mpc}/h)^{-3}$, respectively. The deviation
increases for decreasing $n_P$ and increasing $k$. When $n_P\lesssim 6\times
10^{-4} ({\rm Mpc}/h)^{-3}$, a smoothing effect dominates small scales, causing
significant underestimation of the velocity power spectrum. Second, increasing
$n_k$ helps to recover small scale power. However, for $n_P\lesssim 6\times
10^{-4} ({\rm Mpc}/h)^{-3}$ cases, the recovery is limited. Finally, Kriging is
more sensitive to the variogram prior for lower sample density. The most
straightforward application of Kriging on the cosmic velocity field does not
show obvious advantages over the nearest-particle method (Zheng et al. 2013)
and could not be directly applied to cosmology so far. However, whether
potential improvements may be achieved by more delicate versions of Kriging is
worth further investigation.Comment: 11 pages, 5 figures, published in PR

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