Nonlinearities in modified gravity cosmology. II. Impacts of modified gravity on the halo properties

The statistics of dark matter halos is an essential component of understanding the nonlinear evolution in modified gravity cosmology. Based on a series of modified gravity N-body simulations, we investigate the halo mass function, concentration and bias. We model the impact of modified gravity by a single parameter \zeta, which determines the enhancement of particle acceleration with respect to GR, given the identical mass distribution (\zeta=1 in GR). We select snapshot redshifts such that the linear matter power spectra of different gravity models are identical, in order to isolate the impact of gravity beyond modifying the linear growth rate. At the baseline redshift corresponding to z_S=1.2 in the standard \Lambda CDM, for a 10% deviation from GR(|\zeta-1|=0.1), the measured halo mass function can differ by about 5-10%, the halo concentration by about 10-20%, while the halo bias differs significantly less. These results demonstrate that the halo mass function and/or the halo concentration are sensitive to the nature of gravity and may be used to make interesting constraints along this line.Comment: 8 pages, 7 figures, accepted for publication in Physical Review

The source-lens clustering effect in the context of lensing tomography and its self-calibration

Cosmic shear can only be measured where there are galaxies. This source-lens clustering (SLC) effect has two sources, intrinsic source clustering and cosmic magnification (magnification/size bias). Lensing tomography can suppress the former. However, this reduction is limited by the existence of photo-z error and nonzero redshift bin width. Furthermore, SLC induced by cosmic magnification cannot be reduced by lensing tomography. Through N-body simulations, we quantify the impact of SLC on the lensing power spectrum in the context of lensing tomography. We consider both the standard estimator and the pixel-based estimator. We find that none of them can satisfactorily handle both sources of SLC. (1) For the standard estimator, SLC induced by both sources can bias the lensing power spectrum by O(1)-O(10)%. Intrinsic source clustering also increases statistical uncertainties in the measured lensing power spectrum. However, the standard estimator suppresses intrinsic source clustering in the cross-spectrum. (2) In contrast, the pixel-based estimator suppresses SLC through cosmic magnification. However, it fails to suppress SLC through intrinsic source clustering and the measured lensing power spectrum can be biased low by O(1)-O(10)%. In short, for typical photo-z errors (sigma_z/(1+z)=0.05) and photo-z bin sizes (Delta_z^P=0.2), SLC alters the lensing E-mode power spectrum by 1-10%, with ell~10^3$ and z_s~1 being of particular interest to weak lensing cosmology. Therefore the SLC is a severe systematic for cosmology in Stage-IV lensing surveys. We present useful scaling relations to self-calibrate the SLC effect.Comment: 13 pages, 10 figures, Accepted by AP

The Impact of Baryons on the Large-Scale Structure of the Universe

Numerical simulations play an important role in current astronomy researches. Previous dark-matter-only simulations have represented the large-scale structure of the Universe. However, nowadays, hydro-dynamical simulations with baryonic models, which can directly present realistic galaxies, may twist these results from dark-matter-only simulations. In this chapter, we mainly focus on these three statistical methods: power spectrum, two-point correlation function and halo mass function, which are normally used to characterize the large-scale structure of the Universe. We review how these baryon processes influence the cosmology structures from very large scale to quasi-linear and non-linear scales by comparing dark-matter-only simulations with their hydro-dynamical counterparts. At last, we make a brief discussion on the impacts coming from different baryon models and simulation codes

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 $\kappa$. It performs a local monotonic transformation $\kappa\rightarrow y$ 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 $\kappa$ 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.Comment: 13 pages, 15 figures, accepted by PR

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.Comment: 13 pages, 10 figures. Match the published version. Accepted by PR

Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning

Pair trading is one of the most effective statistical arbitrage strategies which seeks a neutral profit by hedging a pair of selected assets. Existing methods generally decompose the task into two separate steps: pair selection and trading. However, the decoupling of two closely related subtasks can block information propagation and lead to limited overall performance. For pair selection, ignoring the trading performance results in the wrong assets being selected with irrelevant price movements, while the agent trained for trading can overfit to the selected assets without any historical information of other assets. To address it, in this paper, we propose a paradigm for automatic pair trading as a unified task rather than a two-step pipeline. We design a hierarchical reinforcement learning framework to jointly learn and optimize two subtasks. A high-level policy would select two assets from all possible combinations and a low-level policy would then perform a series of trading actions. Experimental results on real-world stock data demonstrate the effectiveness of our method on pair trading compared with both existing pair selection and trading methods.Comment: 10 pages, 6 figure

Tris(2,2′-bipyridine-κ2 N,N′)cadmium(II) bis­(perchlorate) hemihydrate

The asymmetric unit of the title compound, [Cd(C10H8N2)3](ClO4)2·0.5H2O, consists of one complex [Cd(bpy)3]2+ cation (bpy = 2,2′-bipyridine), two perchlorate anions and one water molecule with half-occupancy. The central cadmium(II) ion is bound to six N atoms from three bpy ligands in a distorted octa­hedral coordination, with Cd—N bond distances ranging from 2.304 (3) to 2.395 (2) Å