The Correspondence between Convergence Peaks from Weak Lensing and Massive Dark Matter Haloes

The convergence peaks, constructed from galaxy shape measurement in weak lensing, is a powerful probe of cosmology as the peaks can be connected with the underlined dark matter haloes. However the capability of convergence peak statistic is affected by the noise in galaxy shape measurement, signal to noise ratio as well as the contribution from the projected mass distribution from the large-scale structures along the line of sight (LOS). In this paper we use the ray-tracing simulation on a curved sky to investigate the correspondence between the convergence peak and the dark matter haloes at the LOS. We find that, in case of no noise and for source galaxies at $z_{\rm s}=1$, more than $65\%$ peaks with $\text{SNR} \geq 3$ (signal to noise ratio) are related to more than one massive haloes with mass larger than $10^{13} {\rm M}_{\odot}$. Those massive haloes contribute $87.2\%$ to high peaks ($\text{SNR} \geq 5$) with the remaining contributions are from the large-scale structures. On the other hand, the peaks distribution is skewed by the noise in galaxy shape measurement, especially for lower SNR peaks. In the noisy field where the shape noise is modelled as a Gaussian distribution, about $60\%$ high peaks ($\text{SNR} \geq 5$) are true peaks and the fraction decreases to $20\%$ for lower peaks ($3 \leq \text{SNR} < 5$). Furthermore, we find that high peaks ($\text{SNR} \geq 5$) are dominated by very massive haloes larger than $10^{14} {\rm M}_{\odot}$.Comment: 13 pages, 11 figures, 4 tables, accepted for publication in MNRAS. Our mock galaxy catalog is available upon request by email to the author ([email protected]

Mask Effects on Cosmological Studies with Weak Lensing Peak Statistics

In this paper, we analyze in detail with numerical simulations how the mask effect can influence the weak lensing peak statistics reconstructed from the shear measurement of background galaxies. It is found that high peak fractions are systematically enhanced due to masks, the larger the masked area, the higher the enhancement. In the case with about $13\%$ of the total masked area, the fraction of peaks with SNR $\nu\ge 3$ is $\sim 11\%$ in comparison with $\sim 7\%$ of the mask-free case in our considered cosmological model. This can induce a large bias on cosmological studies with weak lensing peak statistics. Even for a survey area of $9\hbox{ deg}^2$, the bias in $(\Omega_m, \sigma_8)$ is already close to $3\sigma$. It is noted that most of the affected peaks are close to the masked regions. Therefore excluding peaks in those regions can reduce the bias but at the expense of loosing usable survey areas. Further investigations find that the enhancement of high peaks number can be largely attributed to higher noise led by the fewer number of galaxies usable in the reconstruction. Based on Fan et al. (2010), we develop a model in which we exclude only those large masks with radius larger than $3\arcmin. For the remained part, we treat the areas close to and away from the masked regions separately with different noise levels. It is shown that this two-noise-level model can account for the mask effect on peak statistics very well and the cosmological bias is significantly reduced.Comment: 18 pages, 16 figures, ApJ in pres

Blue straggler evolution caught in the act in the Large Magellanic Cloud globular cluster Hodge 11

High-resolution {\sl Hubble Space Telescope} imaging observations show that the radial distribution of the field-decontaminated sample of 162 'blue straggler' stars (BSs) in the $11.7^{+0.2}_{-0.1}$ Gyr-old Large Magellanic Cloud cluster Hodge 11 exhibits a clear bimodality. In combination with their distinct loci in color--magnitude space, this offers new evidence in support of theoretical expectations that suggest different BS formation channels as a function of stellar density. In the cluster's color--magnitude diagram, the BSs in the inner 15$"$ (roughly corresponding to the cluster's core radius) are located more closely to the theoretical sequence resulting from stellar collisions, while those in the periphery (at radii between 85$"$ and 100$"$) are preferentially found in the region expected to contain objects formed through binary mass transfer or coalescence. In addition, the objects' distribution in color--magntiude space provides us with the rare opportunity in an extragalactic environment to quantify the evolution of the cluster's collisionally induced BS population and the likely period that has elapsed since their formation epoch, which we estimate to have occurred $\sim$4--5 Gyr ago.Comment: 13 pages, 4 figure, accepted by Astrophysical Journal Letter

Forecast of cross-correlation of CSST cosmic shear tomography with AliCPT-1 CMB lensing

We present a forecast study on the cross-correlation between cosmic shear tomography from the Chinese Survey Space Telescope (CSST), and CMB lensing from Ali CMB Polarization Telescope (AliCPT-1) in Tibet. We generate the correlated galaxy lensing and CMB lensing signals from the Gaussian realizations based on the inputted auto- and cross-spectra. As for the error budget, we consider the CMB lensing reconstruction noise based on the AliCPT-1 lensing reconstruction pipeline; the shape noise of the galaxy lensing measurement; CSST photo-$z$ error; photo-$z$ bias; intrinsic alignment effect. The AliCPT-1 CMB lensing mock data are generated according to two experimental stages, namely the "4 modules*yr" and "48 modules*yr" cases. We estimate the cross-spectra in 4 tomographic bins according to the CSST photo-$z$ distribution in the range of $z\in[0,4)$. After reconstructing the pseudo-cross-spectra from the realizations, we calculate the signal-to-noise ratio (SNR). By combining the 4 photo-z bins, the total cross-correlation SNR$\approx17$ (AliCPT-1 "4 modules*yr") and SNR$\approx26$ (AliCPT-1 "48 modules*yr"). Finally, we study the cosmological application of this cross-correlation signal. Due to the negative contribution to the galaxy lensing data, the exclusion of intrinsic alignment in the template fitting will lead to roughly a $0.6\sigma$ increasement in $\sigma_8$ but without changing the $S_8$ value. For AliCPT-1 first and second stages, the cross-correlation of CSST cosmic shear with CMB lensing give $\sigma_8=0.770\pm 0.034$ and $S_8=0.797\pm 0.028$ and $\sigma_8=0.801\pm 0.023$ and $S_8=0.813\pm 0.016$, respectively.Comment: 17 pages, 10 figure

Constraining interacting dark energy models with the halo concentration - mass relation

The interacting dark energy (IDE) model is a promising alternative cosmological model which has the potential to solve the fine-tuning and coincidence problems by considering the interaction between dark matter and dark energy. Previous studies have shown that the energy exchange between the dark sectors in this model can significantly affect the dark matter halo properties. In this study, utilising a large set of cosmological $N$-body simulations, we analyse the redshift evolution of the halo concentration - mass ($c$ - $M$) relation in the IDE model, and show that the $c$ - $M$ relation is a sensitive proxy of the interaction strength parameter $\xi_2$, especially at lower redshifts. Furthermore, we construct parametrized formulae to quantify the dependence of the $c$ - $M$ relation on $\xi_2$ at redshifts ranging from $z=0$ to $0.6$. Our parametrized formulae provide a useful tool in constraining $\xi_2$ with the observational $c$ - $M$ relation. As a first attempt, we use the data from X-ray, gravitational lensing, and galaxy rotational curve observations and obtain a tight constraint on $\xi_2$, i.e. $\xi_2 = 0.071 \pm 0.034$. Our work demonstrates that the halo $c$ - $M$ relation, which reflects the halo assembly history, is a powerful probe to constrain the IDE model.Comment: 9 pages, 5 figures, 5 table

Dark matter haloes in interacting dark energy models : formation history, density profile, spin, and shape

The interacting dark energy (IDE) model, which considers the interaction between dark energy and dark matter, provides a natural mechanism to alleviate the coincidence problem and can also relieve the observational tensions under the ?CDM model. Previous studies have put constraints on IDE models by observations of cosmic expansion history, cosmic microwave background, and large-scale structures. However, these data are not yet enough to distinguish IDE models from ?CDM effectively. Because the non-linear structure formation contains rich cosmological information, it can provide additional means to differentiate alternative models. In this paper, based on a set of N-body simulations for IDE models, we investigate the formation histories and properties of dark matter haloes and compare with their ?CDM counterparts. For the model with dark matter decaying into dark energy and the parameters being the best-fitting values from previous constraints, the structure formation is markedly slowed down, and the haloes have systematically lower mass, looser internal structure, higher spin, and anisotropy. This is inconsistent with the observed structure formation, and thus this model can be safely ruled out from the perspective of non-linear structure formation. Moreover, we find that the ratio of halo concentrations between IDE and ?CDM counterparts depends sensitively on the interaction parameter and is independent of halo mass. This can act as a powerful probe to constrain IDE models. Our results concretely demonstrate that the interaction of the two dark components can affect the halo formation considerably, and therefore the constraints from non-linear structures are indispensable.Peer reviewe

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme