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We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous FoF cluster identification in two aspects; 1) we identify cluster halos based on the galaxy subhalos instead of unobservable dark matter particles, and 2) we use the MGS algorithm that separates galaxy overdensities hosted by massive galaxies. Our approach provides a cluster catalog constructed similar to the observed cluster catalogs using spectroscopic surveys. The MGS cluster catalog lists 303 halos with M$_{200} > 10^{14}$ M$_{\odot}$, including $\sim 10\%$ more than the FoF. The MGS catalog includes more systems because we separate some independent massive MGS cluster halos that are bundled into a single FoF algorithm. These independent MGS halos are apparently distinguishable in galaxy spatial distribution and the phase-space diagram. Because we constructed a refined cluster catalog that identifies local galaxy overdensities, we evaluate the effect of MGS clusters on the evolution of galaxies better than using the FoF cluster catalog. The MGS halo identification also enables effective identifications of merging clusters by selecting systems with neighboring galaxy overdensities. We thus highlight that the MGS cluster catalog is a useful tool for studying clusters in cosmological simulations and for comparing with the observed cluster samples., Comment: 15 pages, 9 figures, 2 tables, Submitted to ApJ

One intriguing approach for studying the dynamical evolution of galaxy clusters is to compare the spatial distributions among various components, such as dark matter, member galaxies, gas, and intracluster light (ICL). Utilizing the recently introduced Weighted Overlap Coefficient (WOC) \citep{2022ApJS..261...28Y}, we analyze the spatial distributions of components within 174 galaxy clusters ($M_{\rm tot}> 5 \times 10^{13} M_{\odot}$, $z=0.625$) at varying dynamical states in the cosmological hydrodynamical simulation Horizon Run 5. We observe that the distributions of gas and the combination of ICL with the brightest cluster galaxy (BCG) closely resembles the dark matter distribution, particularly in more relaxed clusters, characterized by the half-mass epoch. The similarity in spatial distribution between dark matter and BCG+ICL mimics the changes in the dynamical state of clusters during a major merger. Notably, at redshifts $>$ 1, BCG+ICL traced dark matter more accurately than the gas. Additionally, we examined the one-dimensional radial profiles of each component, which show that the BCG+ICL is a sensitive component revealing the dynamical state of clusters. We propose a new method that can approximately recover the dark matter profile by scaling the BCG+ICL radial profile. Furthermore, we find a recipe for tracing dark matter in unrelaxed clusters by including the most massive satellite galaxies together with BCG+ICL distribution. Combining the BCG+ICL and the gas distribution enhances the dark matter tracing ability. Our results imply that the BCG+ICL distribution is an effective tracer for the dark matter distribution, and the similarity of spatial distribution may be a useful probe of the dynamical state of a cluster., Comment: 23 pages, 12 figures, accepted for publication in ApJ

We present a novel approach for estimating cosmological parameters, $\Omega_m$, $\sigma_8$, $w_0$, and one derived parameter, $S_8$, from 3D lightcone data of dark matter halos in redshift space covering a sky area of $40^\circ \times 40^\circ$ and redshift range of $0.3 < z < 0.8$, binned to $64^3$ voxels. Using two deep learning algorithms, Convolutional Neural Network (CNN) and Vision Transformer (ViT), we compare their performance with the standard two-point correlation (2pcf) function. Our results indicate that CNN yields the best performance, while ViT also demonstrates significant potential in predicting cosmological parameters. By combining the outcomes of Vision Transformer, Convolution Neural Network, and 2pcf, we achieved a substantial reduction in error compared to the 2pcf alone. To better understand the inner workings of the machine learning algorithms, we employed the Grad-CAM method to investigate the sources of essential information in activation maps of the CNN and ViT. Our findings suggest that the algorithms focus on different parts of the density field and redshift depending on which parameter they are predicting. This proof-of-concept work paves the way for incorporating deep learning methods to estimate cosmological parameters from large-scale structures, potentially leading to tighter constraints and improved understanding of the Universe., Comment: 23 pages, 10 figures

The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter of the Universe, we can reconstruct the full density and velocity fields from the galaxies that act as tracer particles. In this paper, we use the simulated halos as proxies for the galaxies. We use a convolutional neural network, a V-net, trained on numerical simulations of structure formation to reconstruct the density and velocity fields. We find that, with detailed tuning of the loss function, the V-net could produce better fits to the density field in the high-density and low-density regions, and improved predictions for the probability distribution of the amplitudes of the velocities. However, the weights will reduce the precision of the estimated $\beta$ parameter. We also find that the redshift-space distortions of the halo catalogue do not significantly contaminate the reconstructed real-space density and velocity field. We estimate the velocity field $\beta$ parameter by comparing the peculiar velocities of halo catalogues to the reconstructed velocity fields, and find the estimated $\beta$ values agree with the fiducial value at the 68\% confidence level., Comment: 25 pages, 18 figures, 2 tables. Published JCAP

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most efficiently and defines galaxy networks in a way that most closely resembles human vision., Comment: 26 pages, 13 figures

In a galaxy cluster, the relative spatial distributions of dark matter, member galaxies, gas, and intracluster light (ICL) may connote their mutual interactions over the cluster evolution. However, it is a challenging problem to provide a quantitative measure for the shape matching between two multi-dimensional scalar distributions. We present a novel methodology, named the {\em Weighted Overlap Coefficient (WOC)}, to quantify the similarity of 2-dimensional spatial distributions. We compare the WOC with a standard method known as the Modified Hausdorff Distance (MHD). We find that our method is robust, and performs well even with the existence of multiple sub-structures. We apply our methodology to search for a visible component whose spatial distribution resembled with that of dark matter. If such a component could be found to trace the dark matter distribution with high fidelity for more relaxed galaxy clusters, then the similarity of the distributions could also be used as a dynamical stage estimator of the cluster. We apply the method to six galaxy clusters at different dynamical stages simulated within the GRT simulation, which is an N-body simulation using the galaxy replacement technique. Among the various components (stellar particles, galaxies, ICL), the ICL+ brightest cluster galaxy (BCG) component most faithfully trace the dark matter distribution. Among the sample galaxy clusters, the relaxed clusters show stronger similarity in the spatial distribution of the dark matter and ICL+BCG than the dynamically young clusters. While the MHD results show weaker trend with the dynamical stages., Comment: 17 pages, 8 figures, accepted in ApJS

The mark weighted correlation function (MCF) $W(s,\mu)$ is a computationally efficient statistical measure which can probe clustering information beyond that of the conventional 2-point statistics. In this work, we extend the traditional mark weighted statistics by using powers of the density field gradient $|\nabla \rho/\rho|^\alpha$ as the weight, and use the angular dependence of the scale-averaged MCFs to constrain cosmological parameters. The analysis shows that the gradient based weighting scheme is statistically more powerful than the density based weighting scheme, while combining the two schemes together is more powerful than separately using either of them. Utilising the density weighted or the gradient weighted MCFs with $\alpha=0.5,\ 1$, we can strengthen the constraint on $\Omega_m$ by factors of 2 or 4, respectively, compared with the standard 2-point correlation function, while simultaneously using the MCFs of the two weighting schemes together can be $1.25$ times more statistically powerful than using the gradient weighting scheme alone. The mark weighted statistics may play an important role in cosmological analysis of future large-scale surveys. Many issues, including the possibility of using other types of weights, the influence of the bias on this statistics, as well as the usage of MCFs in the tomographic Alcock-Paczynski method, are worth further investigations., Comment: 11 pages, 8 figures. Accepted by MNRAS

We present forecasts on the detectability of Ultra-light axion-like particles (ULAP) from future 21cm radio observations around the epoch of reionization (EoR). We show that the axion as the dominant dark matter component has a significant impact on the reionization history due to the suppression of small scale density perturbations in the early universe. This behavior depends strongly on the mass of the axion particle. Using numerical simulations of the brightness temperature field of neutral hydrogen over a large redshift range, we construct a suite of training data. This data is used to train a convolutional neural network that can build a connection between the spatial structures of the brightness temperature field and the input axion mass directly. We construct mock observations of the future Square Kilometer Array survey, SKA1-Low, and find that even in the presence of realistic noise and resolution constraints, the network is still able to predict the input axion mass. We find that the axion mass can be recovered over a wide mass range with a precision of approximately 20\%, and as the whole DM contribution, the axion can be detected using SKA1-Low at 68\% if the axion mass is $M_X<1.86 \times10^{-20}$eV although this can decrease to $M_X<5.25 \times10^{-21}$eV if we relax our assumptions on the astrophysical modeling by treating those astrophysical parameters as nuisance parameters., Comment: 18 pages, 11 figures, accepted in JCAP

We develop a deep learning technique to infer the non-linear velocity field from the dark matter density field. The deep learning architecture we use is an "U-net" style convolutional neural network, which consists of 15 convolution layers and 2 deconvolution layers. This setup maps the 3-dimensional density field of $32^3$-voxels to the 3-dimensional velocity or momentum fields of $20^3$-voxels. Through the analysis of the dark matter simulation with a resolution of $2 {h^{-1}}{\rm Mpc}$, we find that the network can predict the the non-linearity, complexity and vorticity of the velocity and momentum fields, as well as the power spectra of their value, divergence and vorticity and its prediction accuracy reaches the range of $k\simeq1.4$ $h{\rm Mpc}^{-1}$ with a relative error ranging from 1% to $\lesssim$10%. A simple comparison shows that neural networks may have an overwhelming advantage over perturbation theory in the reconstruction of velocity or momentum fields., Comment: 10 pages, 6 figures, 4 tables, accepted for publication in ApJ

Shortly after its discovery, General Relativity (GR) was applied to predict the behavior of our Universe on the largest scales, and later became the foundation of modern cosmology. Its validity has been verified on a range of scales and environments from the Solar system to merging black holes. However, experimental confirmations of GR on cosmological scales have so far lacked the accuracy one would hope for -- its applications on those scales being largely based on extrapolation and its validity sometimes questioned in the shadow of the unexpected cosmic acceleration. Future astronomical instruments surveying the distribution and evolution of galaxies over substantial portions of the observable Universe, such as the Dark Energy Spectroscopic Instrument (DESI), will be able to measure the fingerprints of gravity and their statistical power will allow strong constraints on alternatives to GR. In this paper, based on a set of $N$-body simulations and mock galaxy catalogs, we study the predictions of a number of traditional and novel estimators beyond linear redshift distortions in two well-studied modified gravity models, chameleon $f(R)$ gravity and a braneworld model, and the potential of testing these deviations from GR using DESI. These estimators employ a wide array of statistical properties of the galaxy and the underlying dark matter field, including two-point and higher-order statistics, environmental dependence, redshift space distortions and weak lensing. We find that they hold promising power for testing GR to unprecedented precision. The major future challenge is to make realistic, simulation-based mock galaxy catalogs for both GR and alternative models to fully exploit the statistic power of the DESI survey and to better understand the impact of key systematic effects. Using these, we identify future simulation and analysis needs for gravity tests using DESI., Comment: 69 pages, 36 figures, 6 tables; accepted for publication in JCAP. The modified gravity HOD galaxy catalogues used in this paper can be downloaded from http://icc.dur.ac.uk/data/, by searching for the title of this paper from the dataset table

We used the mark weighted correlation functions (MCFs), $W(s)$, to study the large scale structure of the Universe. We studied five types of MCFs with the weighting scheme $\rho^\alpha$, where $\rho$ is the local density, and $\alpha$ is taken as $-1,\ -0.5,\ 0,\ 0.5$, and 1. We found that different MCFs have very different amplitudes and scale-dependence. Some of the MCFs exhibit distinctive peaks and valleys that do not exist in the standard correlation functions. Their locations are robust against the redshifts and the background geometry, however it is unlikely that they can be used as ``standard rulers'' to probe the cosmic expansion history. Nonetheless we find that these features may be used to probe parameters related with the structure formation history, such as the values of $\sigma_8$ and the galaxy bias. Finally, after conducting a comprehensive analysis using the full shapes of the $W(s)$s and $W_{\Delta s}(\mu)$s, we found that, combining different types of MCFs can significantly improve the cosmological parameter constraints. Compared with using only the standard correlation function, the combinations of MCFs with $\alpha=0,\ 0.5,\ 1$ and $\alpha=0,\ -1,\ -0.5,\ 0.5,\ 1$ can improve the constraints on $\Omega_m$ and $w$ by $\approx30\%$ and $50\%$, respectively. We find highly significant evidence that MCFs can improve cosmological parameter constraints., Comment: 15pages, 17figures, APJ accepted

The tomographic AP method is so far the best method in separating the Alcock-Paczynski (AP) signal from the redshift space distortion (RSD) effects and deriving powerful constraints on cosmological parameters using the $\lesssim40h^{-1}\ \rm Mpc$ clustering region. To guarantee that the method can be easily applied to the future large scale structure (LSS) surveys, we study the possibility of estimating the systematics of the method using fast simulation method. The major contribution of the systematics comes from the non-zero redshift evolution of the RSD effects, which is quantified by $\hat\xi_{\Delta s}(\mu,z)$ in our analysis, and estimated using the BigMultidark exact N-body simulation and approximate COLA simulation samples. We find about 5\%/10\% evolution when comparing the $\hat\xi_{\Delta s}(\mu,z)$ measured as $z=0.5$/$z=1$ to the measurements at $z=0$. We checked the inaccuracy in the 2pCFs computed using COLA, and find it 5-10 times smaller than the intrinsic systematics of the tomographic AP method, indicating that using COLA to estimate the systematics is good enough. Finally, we test the effect of halo bias, and find $\lesssim$1.5\% change in $\hat\xi_{\Delta s}$ when varying the halo mass within the range of $2\times 10^{12}$ to $10^{14}$ $M_{\odot}$. We will perform more studies to achieve an accurate and efficient estimation of the systematics in redshift range of $z=0-1.5$., Comment: 13 pages, 6 figures. Accepted to ApJ

We propose a light-weight deep convolutional neural network (CNN) to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy. The training set is based on 465 realizations of a cubic box with a side length of $256\ h^{-1}\ \rm Mpc$, sampled with $128^3$ particles interpolated over a cubic grid of $128^3$ voxels. These volumes have cosmological parameters varying within the flat $\Lambda$CDM parameter space of $0.16 \leq \Omega_m \leq 0.46$ and $2.0 \leq 10^9 A_s \leq 2.3$. The neural network takes as an input cubes with $32^3$ voxels and has three convolution layers, three dense layers, together with some batch normalization and pooling layers. In the final predictions from the network we find a $2.5\%$ bias on the primordial amplitude $\sigma_8$ that can not easily be resolved by continued training. We correct this bias to obtain unprecedented accuracy in the cosmological parameter estimation with statistical uncertainties of $\delta \Omega_m$=0.0015 and $\delta \sigma_8$=0.0029, which are several times better than the results of previous CNN works. Compared with a 2-point analysis method using clustering region of 0-130 and 10-130 $h^{-1}$ Mpc, the CNN constraints are several times and an order of magnitude more precise, respectively. Finally, we conduct preliminary checks of the error-tolerance abilities of the neural network, and find that it exhibits robustness against smoothing, masking, random noise, global variation, rotation, reflection, and simulation resolution. Those effects are well understood in typical clustering analysis, but had not been tested before for the CNN approach. Our work shows that CNN can be more promising than people expected in deriving tight cosmological constraints from the cosmic large scale structure., Comment: 17 pages, 10 figures, 1 table

The tomographic Alcock-Paczynski (AP) method utilizes the redshift evolution of the AP distortion to place constraints on cosmological parameters. It has proved to be a robust method that can separate the AP signature from the redshift space distortion (RSD) effect, and deliver powerful cosmological constraints using the $\lesssim 40h^{-1}\ \rm Mpc$ clustering region. In previous works, the tomographic AP method was performed via the anisotropic 2-point correlation function statistic. In this work we consider the feasibility of conducting the analysis in the Fourier domain and examine the pros and cons of this approach. We use the integrated galaxy power spectrum (PS) as a function of direction, $\hat P_{\Delta k}(\mu)$, to quantify the magnitude of anisotropy in the large-scale structure clustering, and use its redshift variation to do the AP test. The method is tested on the large, high resolution Big-MultiDark Planck (BigMD) simulation at redshifts $z=0-1$, using the underlying true cosmology $\Omega_m=0.3071,\ w=-1$. Testing the redshift evolution of $\hat P_{\Delta k}(\mu)$ in the true cosmology and cosmologies deviating from the truth with $\delta \Omega_m=0.1,\ \delta w=0.3$, we find that the redshift evolution of the AP distortion overwhelms the effects created by the RSD by a factor of $\sim1.7-3.6$. We test the method in the range of $k\in(0.2,1.8)\ h\ \rm Mpc^{-1}$, and find that it works well throughout the entire regime. We tune the halo mass within the range $2\times 10^{13}$ to $10^{14}\ M_{\odot}$, and find that the change of halo bias results in $\lesssim 5 \%$ change in $\hat P_{\Delta k}(\mu)$, which is less significant compared with the cosmological effect. Our work shows that it is feasible to conduct the tomographic AP analysis in the Fourier space., Comment: 7 pages, 7 figures

We develop an improved Alcock-Paczynski (AP) test method that uses the redshift-space two-point correlation function (2pCF) of galaxies. Cosmological constraints can be obtained by examining the redshift dependence of the normalized 2pCF, which should not change apart from the expected small non-linear evolution. An incorrect choice of cosmology used to convert redshift to comoving distance will manifest itself as redshift-dependent 2pCF. Our method decomposes the redshift difference of the two-dimensional correlation function into the Legendre polynomials whose amplitudes are modeled by radial fitting functions. Our likelihood analysis with this 2-D fitting scheme tightens the constraints on $\Omega_m$ and ${w}$ by $\sim 40\%$ compared to the method of Li et al. (2016, 2017, 2018) that uses one dimensional angular dependence only. We also find that the correction for the non-linear evolution in the 2pCF has a non-negligible cosmology dependence, which has been neglected in previous similar studies by Li et al.. With an accurate accounting for the non-linear systematics and use of full two-dimensional shape information of the 2pCF down to scales as small as $5~h^{-1}{\rm Mpc}$ it is expected that the AP test with redshift-space galaxy clustering anisotropy can be a powerful method to constrain the expansion history of the universe., Comment: 11 pages, 8 figures, accepted for publication

The tomographic Alcock-Paczynski (AP) method can result in tight cosmological constraints by using small and intermediate clustering scales of the large scale structure (LSS) of the galaxy distribution. By focusing on the redshift dependence, the AP distortion can be distinguished from the distortions produced by the redshift space distortions (RSD). In this work, we combine the tomographic AP method with other recent observational datasets of SNIa+BAO+CMB+$H_0$ to reconstruct the dark energy equation-of-state $w$ in a non-parametric form. The result favors a dynamical DE at $z\lesssim1$, and shows a mild deviation ($\lesssim2\sigma$) from $w=-1$ at $z=0.5-0.7$. We find the addition of the AP method improves the low redshift ($z\lesssim0.7$) constraint by $\sim50\%$., Comment: 8 pages, 3 figures, accepted to ApJ

We present an algorithm for the fast computation of the general $N$-point spatial correlation functions of any discrete point set embedded within an Euclidean space of $\mathbb{R}^n$. Utilizing the concepts of kd-trees and graph databases, we describe how to count all possible $N$-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths $

We perform an anisotropic clustering analysis of 1,133,326 galaxies from the Sloan Digital Sky Survey (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS) Data Release (DR) 12 covering the redshift range $0.15

We develop a methodology to use the redshift dependence of the galaxy 2-point correlation function (2pCF) across the line-of-sight, $\xi(r_{\bot})$, as a probe of cosmological parameters. The positions of galaxies in comoving Cartesian space varies under different cosmological parameter choices, inducing a {\it redshift-dependent scaling} in the galaxy distribution. This geometrical distortion can be observed as a redshift-dependent rescaling in the measured $\xi(r_{\bot})$. We test this methodology using a sample of 1.75 billion mock galaxies at redshifts 0, 0.5, 1, 1.5, 2, drawn from the Horizon Run 4 N-body simulation. The shape of $\xi(r_{\bot})$ can exhibit a significant redshift evolution when the galaxy sample is analyzed under a cosmology differing from the true, simulated one. Other contributions, including the gravitational growth of structure, galaxy bias, and the redshift space distortions, do not produce large redshift evolution in the shape. We show that one can make use of this geometrical distortion to constrain the values of cosmological parameters governing the expansion history of the universe. This method could be applicable to future large scale structure surveys, especially photometric surveys such as DES, LSST, to derive tight cosmological constraints. This work is a continuation of our previous works as a strategy to constrain cosmological parameters using redshift-invariant physical quantities., Comment: 12 pages, 6 figures, Accepted for publication in ApJ

We apply the methodology developed in \cite{Li2014,Li2015} to BOSS DR12 galaxies and derive cosmological constraints from the redshift dependence of the Alcock-Paczynski (AP) effect. The apparent anisotropy in the distribution of observed galaxies arise from two main sources, the redshift-space distortion (RSD) effect due to the galaxy peculiar velocities, and the geometric distortion when incorrect cosmological models are assumed for transforming redshift to comoving distance, known as the AP effect. Anisotropies produced by the RSD effect are, although large, maintaining a nearly uniform magnitude over a large range of redshift, while the degree of anisotropies from the AP effect varies with redshift by much larger magnitude. We split the DR12 galaxies into six redshift bins, measure the 2-point correlation function in each bin, and assess the redshift evolution of anisotropies. We obtain constraints of $\Omega_m=0.290 \pm 0.053,\ \ w = -1.07 \pm 0.15$, which are comparable with the current constraints from other cosmological probes such as type Ia supernovae, cosmic microwave background, and baryon acoustic oscillation (BAO). Combining these cosmological probes with our method yield tight constraints of $ \Omega_m = 0.301 \pm 0.006,\ w=-1.054 \pm 0.025$. Our method is complementary to the other large scale structure probes like BAO and topology. We expect this technique will play an important role in deriving cosmological constraints from large scale structure surveys., Comment: 21 pages, 14 figures. Accepted for publication in ApJ

We present measurements of the spatial clustering statistics in redshift space of various scalar field modified gravity simulations. We utilise the two-point and the three-point correlation functions to quantify the spatial distribution of dark matter halos within these simulations and thus discern between the models. We compare $\Lambda$CDM simulations to various modified gravity scenarios and find consistency with previous work in terms of 2-point statistics in real and redshift-space. However using higher order statistics such as the three-point correlation function in redshift space we find significant deviations from $\Lambda$CDM hinting that higher order statistics may prove to be a useful tool in the hunt for deviations from General Relativity., Comment: 7 pages, 4 figures, updated to match published version, accepted in A&A

The cosmic distance can be precisely determined using a `standard ruler' imprinted by primordial baryon acoustic oscillation (hereafter BAO) in the early Universe. The BAO at the targeted epoch is observed by analysing galaxy clustering in redshift space (hereafter RSD) for which a theoretical formulation is not yet fully understood, and thus makes this methodology unsatisfactory. The BAO analysis following a full RSD modelling is contaminated by systematic uncertainties due to a non-linear smearing effects such as non-linear corrections and by random viral velocity of galaxies. However, the BAO can be probed independently of RSD contamination using the BAO peak positions located in the 2D anisotropic correlation function. A new methodology is presented to measure peak positions, to test whether it is also contaminated by the same systematics in RSD, and to provide the radial and transverse cosmic distances determined by the 2D BAO peak positions. We find that in our model independent anisotropic clustering analysis we can obtain about $2\%$ and $5\%$ constraints on $D_A$ and $H^{-1}$ respectively with current BOSS data, which is competitive with other analysis., Comment: 10 pages, 7 figures

We analyse the clustering of cosmic large scale structure using a consistent modified gravity perturbation theory, accounting for anisotropic effects along and transverse to the line of sight. The growth factor has a particular scale dependence in f(R) gravity and we fit for the shape parameter f_{R0} simultaneously with the distance and the large scale (general relativity) limit of the growth function. Using more than 690,000 galaxies in the Baryon Oscillation Spectroscopy Survey Data Release 11, we find no evidence for extra scale dependence, with the 95\% confidence upper limit |f_{R0}| <8 \times 10^{-4}. Future clustering data, such as from the Dark Energy Spectroscopic Instrument, can use this consistent methodology to impose tighter constraints., Comment: 13 pages, 6 figures

We propose a method using the redshift dependence of the Alcock-Paczynski (AP) test and volume effect to measure the cosmic expansion history. The galaxy two-point correlation function as a function of angle, $\xi(\mu)$, is measured at different redshifts. Assuming an incorrect cosmological model to convert galaxy redshifts to distances, the shape of $\xi(\mu)$ appears anisotropic due to the AP effect, and the amplitude shifted by the change in comoving volume. Due to the redshift dependence of the AP and volume effect, both the shape and amplitude of $\xi(\mu)$ exhibit redshift dependence. Similar to Li et.al (2014), we find the redshift-space distortions (RSD) caused by galaxy peculiar velocities, although significantly distorts $\xi(\mu)$, exhibit much less redshift evolution compared to the AP and volume effects. By focusing on the redshift dependence of $\xi(\mu)$, we can correctly recover the cosmological parameters despite the contamination of RSD. The method is tested by using the Horizon Run 3 N-body simulation, from which we made a series of $1/8$-sky mock surveys having 8 million physically self-bound halos and sampled to have roughly a uniform number density in $z=0-1.5$. We find the AP effect results in tight, unbiased constraints on the density parameter and dark energy equation of state, with 68.3% CL intervals $\delta \Omega_m\sim0.03$ and $\delta w\sim0.1$, and the volume effect leads to much tighter constraints of $\delta \Omega_m\sim0.007$ and $\delta w\sim0.035$., Comment: 9 pages, 5 figures

We present the measurements of the redshift-space three-point correlation function (3PCF) for z~0.5 luminous red galaxies of the CMASS sample in the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Data Release 11. The 3PCF measurements are interpreted within the halo occupation distribution framework using high-resolution N-body simulations, and the model successfully reproduces the 3PCF on scales larger than 1Mpc/h. As with the case for the redshift-space two-point correlation functions, we find that the redshift-space 3PCF measurements also favour the inclusion of galaxy velocity bias in the model. In particular, the central galaxy in a halo is on average in motion with respect to the core of the halo. We discuss the potential of the small-scale 3PCF to tighten the constraints on the relation between galaxies and dark matter haloes and on the phase-space distribution of galaxies., Comment: MNRAS Letter accepted

We analyze the clustering of large scale structure in the Universe in a model independent method, accounting for anisotropic effects along and transverse to the line of sight. The Baryon Oscillation Spectroscopy Survey Data Release 11 provides a large sample of 690,000 galaxies, allowing determination of the Hubble expansion H, angular distance D_A, and growth rate G_T at an effective redshift of z=0.57. After careful bias and convergence studies of the effects from small scale clustering, we find that cutting transverse separations below 40 Mpc/h delivers robust results while smaller scale data leads to a bias due to unmodelled nonlinear and velocity effects. The converged results are in agreement with concordance LCDM cosmology, general relativity, and minimal neutrino mass, all within the 68% confidence level. We also present results separately for the northern and southern hemisphere sky, finding a slight tension in the growth rate -- potentially a signature of anisotropic stress, or just covariance with small scale velocities -- but within 68% CL., Comment: 9 pages, 6 figures

We present a one per cent measurement of the cosmic distance scale from the detections of the baryon acoustic oscillations in the clustering of galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS), which is part of the Sloan Digital Sky Survey III (SDSS-III). Our results come from the Data Release 11 (DR11) sample, containing nearly one million galaxies and covering approximately $8\,500$ square degrees and the redshift range $0.2

Our observations of the Universe are fundamentally anisotropic, with data from galaxies separated transverse to the line of sight coming from the same epoch while that from galaxies separated parallel to the line of sight coming from different times. Moreover, galaxy velocities along the line of sight change their redshift, giving redshift space distortions. We perform a full two-dimensional anisotropy analysis of galaxy clustering data, fitting in a substantially model independent manner the angular diameter distance D_A, Hubble parameter H, and growth rate ddelta/dln a without assuming a dark energy model. The results demonstrate consistency with LCDM expansion and growth, hence also testing general relativity. We also point out the interpretation dependence of the effective redshift z_eff, and its cosmological impact for next generation surveys., Comment: 12 pages, 14 figures

The Sloan Digital Sky Survey (SDSS) has been in operation since 2000 April. This paper presents the tenth public data release (DR10) from its current incarnation, SDSS-III. This data release includes the first spectroscopic data from the Apache Point Observatory Galaxy Evolution Experiment (APOGEE), along with spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS) taken through 2012 July. The APOGEE instrument is a near-infrared R~22,500 300-fiber spectrograph covering 1.514--1.696 microns. The APOGEE survey is studying the chemical abundances and radial velocities of roughly 100,000 red giant star candidates in the bulge, bar, disk, and halo of the Milky Way. DR10 includes 178,397 spectra of 57,454 stars, each typically observed three or more times, from APOGEE. Derived quantities from these spectra (radial velocities, effective temperatures, surface gravities, and metallicities) are also included.DR10 also roughly doubles the number of BOSS spectra over those included in the ninth data release. DR10 includes a total of 1,507,954 BOSS spectra, comprising 927,844 galaxy spectra; 182,009 quasar spectra; and 159,327 stellar spectra, selected over 6373.2 square degrees., Comment: 15 figures; 1 table. Accepted to ApJS. DR10 is available at http://www.sdss3.org/dr10 v3 fixed 3 diacritic markings in the arXiv HTML listing of the author names

We present new measurements of the coherent motion of galaxies based on observations of the large-scale redshift-space distortions seen in the two-dimensional two-point correlation function of Luminous Red Galaxies in Data Release Seven of the Sloan Digital Sky Survey. We have developed a new methodology for estimating these coherent motions, which is less dependent on the details of galaxy bias and of the cosmological model to explain the late-time acceleration of the expansion of the Universe. We measure a one-dimensional velocity dispersion of galaxies on large-scales of \sigma_v=3.01^{+0.45}_{-0.46} Mpc/h and \sigma_v=3.69^{+0.47}_{-0.47} \mpcoh$ at a mean redshift of z=0.25 and 0.38 respectively. These values are fully consistent with predictions for a WMAP7-normalised LCDM Universe and inconsistent at confidence of 3.8\sigma with a Dvali-Gabadadze-Porrati (DGP) model for the Universe. We can convert the units of these $\sigma_v$ measurements to 270^{+40}_{-41} km/s and 320^{+41}_{-41} km/s respectively (assuming a $\Lambda$CDM universe), which are lower that expected based on recent low redshift (z<0.2) measurements of the peculiar velocity field (or "bulk flows"). It is difficult to directly compare these measurements as they cover different redshift ranges and different areas of the sky. However, one possible cosmological explanation for this discrepancy is that our Galaxy is located in unusually over, or under, dense region of the Universe., Comment: 11 pages, 7 figures

We present here a new parameterization for the bulk motions of galaxies and clusters (in the linear regime) that can be measured statistically from the shape and amplitude of the two-dimensional two-point correlation function. We further propose the one-dimensional velocity dispersion (v_p) of the bulk flow as a complementary measure of redshift-space distortions, which is model-independent and not dependent on the normalisation method. As a demonstration, we have applied our new methodology to the C4 cluster catalogue constructed from Data Release Three (DR3) of the Sloan Digital Sky Survey. We find v_p=270^{+433}km/s (also consistent with v_p=0) for this cluster sample (at z=0.1), which is in agreement with that predicted for a WMAP5-normalised LCDM model (i.e., v_p(LCDM=203km/s). This measurement does not lend support to recent claims of excessive bulk motions (\simeq1000 km/s) which appear in conflict with LCDM, although our large statistical error cannot rule them out. From the measured coherent evolution of v_p, we develop a technique to re-construct the perturbed potential, as well as estimating the unbiased matter density fluctuations and scale--independent bias., Comment: 8 pages, 5 figures