arXiv subjects

Yu Yu

Publications and source records attributed to Yu Yu.

112 records · Page 7Linked to original sources

arXiv2014

Satellite Alignment: I. Distribution of Substructures and Their Dependence On Assembly History From N-Body Simulations

Observations have shown that the spatial distribution of satellite galaxies is not random, but aligned with the major axes of central galaxies. This alignment is dependent on galaxy properties, such that red satellites are more strongly aligned than blue satellites. Theoretical work done to interpret this phenomena has found that it is due to the non-spherical nature of dark matter halos. However, most studies over-predict the alignment signal under the assumption that the central galaxy shape follows the shape of the host halo. It is also not clear whether the color dependence of alignment is due to an assembly bias or an evolution effect. In this paper we study these problems using a cosmological N-body simulation. Subhalos are used to trace the positions of satellite galaxies. It is found that the shape of dark matter halos are mis-aligned at different radii. If the central galaxy shares the same shape as the inner host halo, then the alignment effect is weaker and agrees with observational data. However, it predicts almost no dependence of alignment on the color of satellite galaxies, though the late accreted subhalos show stronger alignment with the outer layer of the host halo than their early accreted counterparts. We find that this is due to the limitation of pure N-body simulations that satellites galaxies without associated subhalos ('orphan galaxies') are not resolved. These orphan (mostly red) satellites often reside in the inner region of host halos and should follow the shape of the host halo in the inner region.

astro-ph.CO↗

arXiv2013

Weak lensing reconstruction through cosmic magnification. II. Improved power spectrum determination and map-making

The existence of galaxy intrinsic clustering severely hampers the weak lensing reconstruction from cosmic magnification. In paper I \citep{Yang2011}, we proposed a minimal variance estimator to overcome this problem. By utilizing the different dependences of cosmic magnification and galaxy intrinsic clustering on galaxy flux, we demonstrated that the otherwise overwhelming galaxy intrinsic clustering can be significantly suppressed such that lensing maps can be reconstructed with promising accuracy. This procedure relies heavily on the accuracy of determining the galaxy bias from the same data. Paper I adopts an iterative approach, which degrades toward high redshift. The current paper presents an alternative method, improving over paper I. We prove that the measured galaxy clustering between flux bins allows for simultaneous determination of the lensing power spectrum and the flux dependence of galaxy bias, at this redshift bin. Comparing to paper I, the new approach is not only more straightforward, but also more robust. It identifies an ambiguity in determining the galaxy bias and further discovers a mathematically robust way to suppress this ambiguity to non-negligible level ($\sim 0.1%$). The accurately determined galaxy bias can then be applied to the minimal variance estimator proposed in paper I to improve the lensing map-making. The gain at high redshift is significant. These maps can be used to measure other statistics, such as cluster finding and peak statistics. Furthermore, by including galaxy clustering measurement between different redshift bins, we can also determine the lensing cross power spectrum between these bins, up to a small and correctable multiplicative factor.

astro-ph.CO↗

arXiv2012

Gaussianizing the non-Gaussian lensing convergence field II: the applicability to noisy data

In paper I (Yu et al. [1]), we show through N-body simulation that a local monotonic Gaussian transformation can significantly reduce non-Gaussianity in a noise-free lensing convergence field. This makes the Gaussianization a promising theoretical tool to understand high-order lensing statistics. Here we present a study of its applicability in lensing data analysis, in particular when shape measurement noise is presented in lensing convergence maps. (i) We find that shape measurement noise significantly degrades the Gaussianization performance and the degradation increases for shallower surveys. (ii) The Wiener filter is efficient in reducing the impact of shape measurement noise. The Gaussianization of the Wiener-filtered lensing maps is able to suppress skewness, kurtosis, and the 5th- and 6th-order cumulants by a factor of 10 or more. It also works efficiently to reduce the bispectrum to zero.

astro-ph.CO↗

arXiv2011

Gaussianizing the non-Gaussian lensing convergence field I: the performance of the Gaussianization

Motivated by recent works of Neyrinck et al. 2009 and Scherrer et al. 2010, we proposed a Gaussianization transform to Gaussianize the non-Gaussian lensing convergence field $κ$. It performs a local monotonic transformation $κ\rightarrow y$ pixel by pixel to make the unsmoothed one-point probability distribution function of the new variable $y$ Gaussian. We tested whether the whole $y$ field is Gaussian against N-body simulations. (1) We found that the proposed Gaussianization suppresses the non-Gaussianity by orders of magnitude, in measures of the skewness, the kurtosis, the 5th- and 6th-order cumulants of the $y$ field smoothed over various angular scales relative to that of the corresponding smoothed $κ$ field. The residual non-Gaussianities are often consistent with zero within the statistical errors. (2) The Gaussianization significantly suppresses the bispectrum. Furthermore, the residual scatters around zero, depending on the configuration in the Fourier space. (3) The Gaussianization works with even better performance for the 2D fields of the matter density projected over $\sim 300 \mpch$ distance interval centered at $z\in(0,2)$, which can be reconstructed from the weak lensing tomography. (4) We identified imperfectness and complexities of the proposed Gaussianization. We noticed weak residual non-Gaussianity in the $y$ field. We verified the widely used logarithmic transformation as a good approximation to the Gaussianization transformation. However, we also found noticeable deviations.

astro-ph.CO↗