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The MUltiplexed Survey Telescope (MUST) is a 6.5-meter telescope under development. Dedicated to highly-multiplexed, wide-field spectroscopic surveys, MUST observes over 20,000 targets simultaneously using 6.2-mm pitch positioning robots within a ~5 deg2 field of view. MUST aims to carry out the first Stage-V spectroscopic survey in the 2030s to map the 3D Universe with over 100 million galaxies and quasars, spanning from the nearby Universe to redshift z~5.5, corresponding to around 1 billion years after the Big Bang. To cover this extensive redshift range, we present an initial conceptual target selection algorithm for different types of galaxies, from local bright galaxies, luminous red galaxies, and emission line galaxies to high-redshift (2 < z < 5.5) Lyman-break galaxies. Using Fisher forecasts, we demonstrate that MUST can address fundamental questions in cosmology, including the nature of dark energy, test of gravity theories, and investigations into primordial physics. This is the first paper in the series of science white papers for MUST, with subsequent developments focusing on additional scientific cases such as galaxy and quasar evolution, Milky Way physics, and dynamic phenomena in the time-domain Universe., Comment: To be submitted to SCPMA

Foreground removal presents a significant obstacle in both current and forthcoming intensity mapping surveys. While numerous techniques have been developed that show promise in simulated datasets, their efficacy often diminishes when applied to real-world data. A primary issue is the frequency-dependent variations in the instrumental response. In this paper, we propose a novel approach utilizing the internal cross-correlation among different frequencies to calibrate the beam's frequency fluctuations. Using a simulated dataset that incorporates frequency-dependent random fluctuations into the beam model, we illustrate that our method can achieve considerable improvements over traditional techniques. Our results represent a step forward in enhancing the precision and reliability of foreground removal in intensity mapping surveys., Comment: Accepted for publication in ApJS

We propose a lightweight deep convolutional neural network (lCNN) to estimate cosmological parameters from simulated three-dimensional dark matter (DM) halo distributions and associated statistics. The training dataset comprises 2000 realizations of a cubic box with a side length of 1000 $h^{-1}{\rm Mpc}$, and interpolated over a cubic grid of $300^3$ voxels, with each simulation produced using $512^3$ DM particles and $512^3$ neutrinos. Under the flat $\Lambda$CDM model, simulations vary standard six cosmological parameters including $\Omega_m$, $\Omega_b$, $h$, $n_s$, $\sigma_8$, $w$, along with the neutrino mass sum, $M_\nu$. We find that: 1) within the framework of lCNN, extracting large-scale structure information is more efficient from the halo density field compared to relying on the statistical quantities including the power spectrum, the two-point correlation function, and the coefficients from wavelet scattering transform; 2) combining the halo density field with its Fourier transformed counterpart enhances predictions, while augmenting the training dataset with measured statistics further improves performance; 3) achieving high accuracy in inferring $\Omega_m$, $h$, and $\sigma_8$ by the neural network model, while being inefficient in predicting $\Omega_b$, { $n_s$}, $M_\nu$ and $w$; 4) { compared to the simple fully connected network trained with three statistical quantities, our CNN yields statistically reduced errors, showing improvements of approximately 23\% for $\Omega_m$, 11\% for $h$, 8\% for $n_s$, and 21\% for $\sigma_8$. Additionally, in comparison with the likelihood-based analysis on $P(k)$ data, our CNN provides much tighter constraints on parameters, especially on $\Omega_m$ and $\sigma_8$.} Our study emphasizes this lCNN-based novel approach in extracting large-scale structure information and estimating cosmological parameters., Comment: v2: matches the version published in PRD,17 pages,12 figures

This paper introduces ASTRA (Algorithm for Stochastic Topological RAnking), a novel method designed for the classification of galaxies into principal cosmic web structures -- voids, sheets, filaments, and knots -- especially tailored for large spectroscopic surveys. ASTRA utilizes observed data and a corresponding random catalog, generating probabilities for data and random points to belong to each cosmic web type. Extensive evaluation includes testing on datasets from N-body simulations (dark matter only and hydrodynamical) and observations, incorporating visual inspections and quantitative analyses of mass and volume filling fractions. ASTRA demonstrates proficiency in constructing void size function catalogs and is benchmarked against established cosmic web classification methods. Spatial correlations among cosmic web types are explored using 2-point correlation functions. ASTRA's approach to the cosmic web holds promise for enhancing the retrieval of non-standard clustering statistics, thereby advancing the estimation of cosmological parameters. With simplicity, speed, and applicability across various datasets, ASTRA is a valuable tool in the quantitative study of the large-scale structure of the Universe., Comment: 12 pages, 9 figures. Comments welcome

The $\beta$-skeleton approach can be conveniently utilized to construct the cosmic web based on the spatial geometry distribution of galaxies, particularly in sparse samples. This method plays a key role in establishing the three-dimensional structure of the Universe and serves as a tool for quantitatively characterizing the nature of the cosmic web. This study is the first application of $\beta$-skeleton information as weights in mark weighted correlation functions (MCFs), presenting a novel statistical measure. We have applied the $\beta$-skeleton approach to the CMASS NGC galaxy samples from SDSS BOSS DR12 in the redshift interval $0.45 \leq z \leq 0.55$. Additionally, we applied this approach to three COLA cosmological simulations with different settings ($\Omega_m=0.25, \Omega_m=0.31, \Omega_m=0.4$) for comparison. We measured three MCFs, each weighted by i) the number of neighboring galaxies around each galaxy, ii) the average distance of each galaxy from its surrounding neighbors, and iii) the reciprocal of the average distance of each galaxy from its surrounding neighbors. By comparing measurements and calculating corresponding $\chi^2$ statistics, we observe high sensitivity to the cosmological parameter $\Omega_m$ through a joint analysis of the two-point correlation and three MCFs., Comment: 16 pages,12 figues

The spectroscopic survey of China's Space Survey Telescope (CSST) is expected to obtain a huge number of slitless spectra, including more than one hundred million galaxy spectra and millions of active galactic nuclei (AGN) spectra. By making use of these spectra, we can measure the Baryon Acoustic Oscillation (BAO) signals over large redshift ranges with excellent precisions. In this work, we predict the CSST measurements of the post-reconstruction galaxy power spectra at $0

We investigate the feasibility of using COmoving Lagrangian Acceleration (COLA) technique to efficiently generate galaxy mock catalogues that can accurately reproduce the statistical properties of observed galaxies. Our proposed scheme combines the subhalo abundance matching (SHAM) procedure with COLA simulations, utilizing only three free parameters: the scatter magnitude ($\sigma_{\rm scat}$) in SHAM, the initial redshift ($z_{\rm init}$) of the COLA simulation, and the time stride ($da$) used by COLA. In this proof-of-concept study, we focus on a subset of BOSS CMASS NGC galaxies within the redshift range $z\in [0.45, 0.55]$. We perform $\mathtt{GADGET}$ simulation and low-resolution COLA simulations with various combinations of $(z_{\rm init}, da)$, each using $1024^{3}$ particles in an $800~h^{-1}{\rm Mpc}$ box. By minimizing the difference between COLA mock and CMASS NGC galaxies for the monopole of the two-point correlation function (2PCF), we obtain the optimal $\sigma_{\rm scat}$. We have found that by setting $z_{\rm init}=29$ and $da=1/30$, we achieve a good agreement between COLA mock and CMASS NGC galaxies within the range of 4 to $20~h^{-1}{\rm Mpc}$, with a computational cost two orders of magnitude lower than that of the N-body code. Moreover, a detailed verification is performed by comparing various statistical properties, such as anisotropic 2PCF, three-point clustering, and power spectrum multipoles, which shows similar performance between GADGET mock and COLA mock catalogues with the CMASS NGC galaxies. Furthermore, we assess the robustness of the COLA mock catalogues across different cosmological models, demonstrating consistent results in the resulting 2PCFs. Our findings suggest that COLA simulations are a promising tool for efficiently generating mock catalogues for emulators and machine learning analyses in exploring the large-scale structure of the Universe., Comment: 24 pages, 14 figures, 4 tables

Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical datasets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the Entropy Method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the Entropy Method on the CFHTLenS and DECaLS DR3 data. In CFHTLenS, with 1171 exposures, the Entropy Method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the Entropy method detects a significant number of anomalous exposures while keeping a low false positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the Entropy Method.

The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster but approximate ones. The model error introduced by this substitution can be mitigated in various ways, one of which is by interpolating and marginalizing over the error using Gaussian process regression. However, the use of Gaussian process regression is limited by the curse of dimensionality, which makes it less effective for analyzing higher-dimensional parameter spaces and longer signal durations. In this work, to address this limitation, we focus on gravitational-wave signals from extreme-mass-ratio inspirals as an example, and propose several significant improvements to the base method: an improved prescription for constructing the training set, GPU-accelerated training algorithms, and a new likelihood that better adapts the base method to the presence of detector noise. Our results suggest that the new method is more viable for the analysis of realistic gravitational-wave data.

The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct the peculiar velocity field from the redshift-space distribution of dark matter halos. Through a point-to-point comparison and examination of various statistical properties, we demonstrate that, the reconstructed velocity field is in good agreement with the ground truth. The power spectra of various velocity field components, including velocity magnitude, divergence and vorticity, can be successfully recovered when $k\lesssim 1.1$ $h/\rm Mpc$ (the Nyquist frequency of the simulations) at about 80% accuracy. This approach is very promising and presents an alternative method to correct the redshift-space distortions using the measured 3D spatial information of halos. Additionally, for the reconstruction of the momentum field of halos, UNet achieves similar good results. Hence the applications in various aspects of cosmology are very broad, such as correcting redshift errors and improving measurements in the structure of the cosmic web, the kinetic Sunyaev-Zel'dovich effect, BAO reconstruction, etc., Comment: 15 pages, 13 figures, 4 tables

The star formation history (SFH) of galaxies is critical for understanding galaxy evolution. Hydrodynamical simulations enable us to precisely reconstruct the SFH of galaxies and establish a link to the underlying physical processes. In this work, we present a model to describe individual galaxies' SFHs from three simulations: TheThreeHundred, Illustris-1 and TNG100-1. This model divides the galaxy SFH into two distinct components: the "main sequence" and the "variation". The "main sequence" part is generated by tracing the history of the $SFR-M_*$ main sequence of galaxies across time. The "variation" part consists of the scatter around the main sequence, which is reproduced by fractional Brownian motions. We find that: 1) The evolution of the main sequence varies between simulations; 2) fractional Brownian motions can reproduce many features of SFHs, however, discrepancies still exist; 3) The variations and mass-loss rate are crucial for reconstructing the SFHs of the simulations. This model provides a fair description of the SFHs in simulations. On the other hand, by correlating the fractional Brownian motion model to simulation data, we provide a 'standard' against which to compare simulations., Comment: 22 pages, 18 figures, 2 tables

As one of Stage IV space-based telescopes, China Space Station Telescope (CSST) can perform photometric and spectroscopic surveys simultaneously to efficiently explore the Universe in extreme precision. In this work, we investigate several powerful CSST cosmological probes, including cosmic shear, galaxy-galaxy lensing, photometric and spectroscopic galaxy clustering, and number counts of galaxy clusters, and study the capability of these probes by forecasting the results of joint constraints on the cosmological parameters. By referring to real observational results, we generate mock data and estimate the measured errors based on CSST observational and instrumental designs. To study the systematical effects on the results, we also consider a number of systematics in CSST photometric and spectroscopic surveys, such as the intrinsic alignment, shear calibration uncertainties, photometric redshift uncertainties, galaxy bias, non-linear effects, instrumental effects, etc. The Fisher matrix method is used to derive the constraint results from individual or joint surveys on the cosmological and systematical parameters. We find that the joint constraints by including all these CSST cosmological probes can significantly improve the results from current observations by one order of magnitude at least, which gives $\Omega_m$ and $\sigma_8$ $<$1% accuracy, and $w_0$ and $w_a$ $<$5% and 20% accuracies, respectively. This indicates that the CSST photometric and spectroscopic multi-probe surveys could provide powerful tools to explore the Universe and greatly improve the studies of relevant cosmological problems., Comment: 17 pages, 12 figures, 3 tables. Accepted for publication in MNRAS

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 investigate impacts of massive neutrinos on the cosmic velocity fields, employing high-resolution cosmological $N$-body simulations provided by the information-optimized CUBE code, where cosmic neutrinos are evolved using collisionless hydrodynamics and their perturbations can be accurately resolved. In this study we focus, for the first time, on the analysis of massive-neutrino induced suppression effects in various cosmic velocity field components of velocity magnitude, divergence, vorticity and dispersion. By varying the neutrino mass sum $M_\nu$ from 0 -- 0.4 eV, the simulations show that, the power spectra of vorticity -- exclusively sourced by non-linear structure formation that is affected by massive neutrinos significantly -- is very sensitive to the mass sum, which potentially provide novel signatures in detecting massive neutrinos. Furthermore, using the chi-square statistic, we quantitatively test the sensitivity of the density and velocity power spectra to the neutrino mass sum. Indeed, we find that, the vorticity spectrum has the highest sensitivity, and the null hypothesis of massless neutrinos is incompatible with both vorticity and divergence spectra from $M_\nu=0.1$ eV at high significance ($p$-value $= 0.03$ and $0.07$, respectively). These results demonstrate clearly the importance of peculiar velocity field measurements, in particular of vorticity and divergence components, in determination of neutrino mass and mass hierarchy., Comment: 13 pages, 9 figures. Monthly Notices of the Royal Astronomical Society, 2022

Precise cosmic web classification of observed galaxies in massive spectroscopic surveys can be either highly uncertain or computationally expensive. As an alternative, we explore a fast Machine Learning-based approach to infer the underlying dark matter tidal cosmic web environment of a galaxy distribution from its $\beta$-skeleton graph. We develop and test our methodology using the cosmological magnetohydrodynamic simulation Illustris-TNG at $z=0$. We explore three different tree-based machine-learning algorithms to find that a random forest classifier can best use graph-based features to classify a galaxy as belonging to a peak, filament or sheet as defined by the T-Web classification algorithm. The best match between the galaxies and the dark matter T-Web corresponds to a density field smoothed over scales of $2$ Mpc, a threshold over the eigenvalues of the dimensionless tidal tensor of $\lambda_{\rm{th}}=0.0$ and galaxy number densities around $8\times 10^{-3}$ Mpc$^{-3}$. This methodology results on a weighted F1 score of 0.728 and a global accuracy of 74\%. More extensive tests that take into account lightcone effects and redshift space distortions (RSD) are left for future work. We make one of our highest ranking random forest models available on a public repository for future reference and reuse., Comment: ApJ accepted, 13 pages, 9 figures

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

Space-borne gravitational wave detectors like TianQin are expected to detect GW signals emitted by the mergers of massive black hole binaries. Luminosity distance information can be obtained from GW observations, and one can perform cosmological inference if redshift information can also be extracted, which would be straightforward if an electromagnetic counterpart exists. In this paper, we concentrate on the conservative scenario where the EM counterparts are not available, and comprehensively study if cosmological parameters can be inferred through a statistical approach, utilizing the non-uniform distribution of galaxies as well as the black hole mass-host galaxy bulge luminosity relationship. By adopting different massive black hole binary merger models, and assuming different detector configurations, we conclude that the statistical inference of cosmological parameters is indeed possible. TianQin is expected to constrain the Hubble constant to a relative error of about 4%-7%, depending on the underlying model. The multidetector network of TianQin and LISA can significantly improve the precision of cosmological parameters. In the most favorable model, it is possible to achieve a level of 1.7% with a network of TianQin and LISA. We find that without EM counterparts, constraints on all other parameters need a larger number of events or more precise sky localization of GW sources, which can be achieved by the multidetector network or under a favorable model for massive black hole mergers. However, in the optimistic case, where EM counterparts are available, one can obtain useful constraints on all cosmological parameters in the Lambda-CDM cosmology, regardless of the population model. Moreover, we can also constrain the equation of state of the dark energy without the EM counterparts, and it is even possible to study the evolution of EoS of the DE when the EM counterparts are observed., Comment: 34 pages, 17 figures, update to match published version

We study the role of gravitational waves (GW) in the heat death of the universe. Due to the GW emission, in a very long period, dynamical systems in the universe suffer from persistent mechanical energy dissipation, evolving to a state of universal rest and death. With N-body simulations, we adopt a simple yet representative scheme to calculate the energy loss due to the GW emission. For current dark matter systems with mass $\sim10^{12}-10^{15} M_\odot$, we estimate their GW emission timescale as $\sim10^{19}-10^{25}$ years. This timescale is significantly larger than any baryon processes in the universe, but still $\sim10^{80}$ times shorter than that of the Hawking radiation. We stress that our analysis could be invalid due to many unknowns such as the dynamical chaos, the quadrupole momentum of halos, the angular momentum loss, the dynamic friction, the central black hole accretion, the dark matter decays or annihilations, the property of dark energy and the future evolution of the universe., Comment: Accepted for publication in MNRAS. 6 pages, 4 figures

We explore the information theory entropy of a graph as a scalar to quantify the cosmic web. We find entropy values in the range between 1.5 and 3.2 bits. We argue that this entropy can be used as a discrete analogue of scalars used to quantify the connectivity in continuous density fields. After showing that the entropy clearly distinguishes between clustered and random points, we use simulations to gauge the influence of survey geometry, cosmic variance, redshift space distortions, redshift evolution, cosmological parameters and spatial number density. Cosmic variance shows the least important influence while changes from the survey geometry, redshift space distortions, cosmological parameters and redshift evolution produce larger changes on the order of $10^{-2}$ bits. The largest influence on the graph entropy comes from changes in the number density of clustered points. As the number density decreases, and the cosmic web is less pronounced, the entropy can diminish up to 0.2 bits. The graph entropy is simple to compute and can be applied both to simulations and observational data from large galaxy redshift surveys; it is a new statistic that can be used in a complementary way to other kinds of topological or clustering measurements., Comment: 5 pages, 4 figures, Accepted for publication in MNRAS

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) test is a robust large-scale structure (LSS) measurement that receives little contamination from the redshift space distortion (RSD). It has placed tight cosmological constraints by using small and intermediate clustering scales of the LSS data. However, previous works have neglected the cross-correlation among different redshift bins, which could cause the statistical uncertainty being underestimated by $\sim$20\%. In this work, we further improve this method by including this multi-redshifts full correlation. We apply it to the SDSS DR12 galaxies sample and find out that, for $\Lambda$CDM, the combination of AP with the Planck+BAO dataset slightly reduces (within 1-$\sigma$) $\Omega_m$ to $0.304\pm0.007$ (68.3\% CL). This then leads to a larger $H_0$ and also mildly affects $\Omega_b h^2$, $n_s$ and the derived parameters $z_*$, $r_*$, $z_{re}$ but not $\tau$, $A_s$ and $\sigma_8$. For the flat $w$CDM model, our measurement gives $\Omega_m=0.301\pm 0.010$ and $w=-1.090\pm 0.047$, where the additional AP measurement reduces the error budget by $\sim 25\%$. When including more parameters into the analysis, the AP method also improves the constraints on $\Omega_k$, $\sum m_\mu$, $N_{\rm eff}$ by $20-30\%$. Early universe parameters such as $dn_s/d{\rm ln}k$ and $r$, however, are unaffected. Assuming the dark energy equation of state $w=w_0+w_a \frac{z}{1+z}$, the Planck+BAO+SNIa+$H_0$+AP datasets prefer a dynamical dark energy at $\approx1.5 \sigma$ CL. Finally, we forecast the cosmological constraints expected from the DESI galaxy survey and find that combining AP with CMB+BAO method would improve the $w_0$-$w_a$ constraint by a factor of $\sim 10$., Comment: 13 pages, 12 figures, 3 tables. Accepted for publication in ApJ

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

The Chinese Space Station Optical Survey (CSS-OS) is a planned full sky survey operated by the Chinese Space Station Telescope (CSST). It can simultaneously perform the photometric imaging and spectroscopic slitless surveys, and will probe weak and strong gravitational lensing, galaxy clustering, individual galaxies and galaxy clusters, active galactic nucleus (AGNs), and so on. It aims to explore the properties of dark matter and dark energy and other important cosmological problems. In this work, we focus on two main CSS-OS scientific goals, i.e. the weak gravitational lensing (WL) and galaxy clustering surveys. We generate the mock CSS-OS data based on the observational COSMOS and zCOSMOS catalogs. We investigate the constraints on the cosmological parameters from the CSS-OS using the Markov Chain Monte Carlo (MCMC) method. The intrinsic alignments, galaxy bias, velocity dispersion, and systematics from instrumental effects in the CSST WL and galaxy clustering surveys are also included, and their impacts on the constraint results are discussed. We find that the CSS-OS can improve the constraints on the cosmological parameters by a factor of a few (even one order of magnitude in the optimistic case), compared to the current WL and galaxy clustering surveys. The constraints can be further enhanced when performing joint analysis with the WL, galaxy clustering, and galaxy-galaxy lensing data. Therefore, the CSS-OS is expected to be a powerful survey for exploring the Universe. Since some assumptions may be still optimistic and simple, it is possible that the results from the real survey could be worse. We will study these issues in details with the help of simulations in the future., Comment: 19 pages, 19 figures, 2 tables, accepted for publication in 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 $

The $\beta$-skeleton is a mathematical method to construct graphs from a set of points that has been widely applied in the areas of image analysis, machine learning, visual perception, and pattern recognition. In this work, we apply the $\beta$-skeleton to study the cosmic web. We use this tool on observed and simulated data to identify the filamentary structures and characterize the statistical properties of the skeleton. In particular, we compare the $\beta$-skeletons built from SDSS-III galaxies to those obtained from MD-PATCHY mocks, and also to mocks directly built from the Big MultiDark $N$-body simulation. We find that the $\beta$-skeleton is able to reveal the underlying structures in observed and simulated samples without any parameter fine-tuning. A different degree of sparseness can be obtained by adjusting the value of $\beta$; in addition, the statistical properties of the length and direction of the skeleton connections show a clear dependence on redshift space distortions (RSDs), cosmological effects and galaxy bias. We also find that the $N$-body simulation accurately reproduces the RSD effect in the data, while the MD-PATCHY mocks appear to underestimate its magnitude. Our proof-of-concept study shows that the statistical properties of the $\beta$-skeleton can be used to probe cosmological parameters and galaxy evolution., Comment: 11 pages, 9 figures. Submitted to MNRAS

We study the gravitational wave (GW) production induced by the asymmetric jets of gamma-ray bursts (GRBs). The asymmetric jets result in a recoil force acted on the central compact object, whose motion leads to emission of GW. Under reasonable assumptions and simplifications, we derive the analytic form of the produce GWs. The amplitude of emitted GWs is estimated to be relatively low, but possibility exists that they can be detected by future experiments such as the Einstein Telescope. We find the dynamical properties of the central object, which is difficult to be studied via the electromagnetic (EW) channel, can be inferred by measuring the emitted GWs. Moreover, we find the emitted GWs can be used determine whether the relativistic jets is launched by the neutrino annihilation process or the Blandford-Znajek process, which cannot be clearly distinguished by the current GRB observations. Our work manifests the importance of the GW channel in multi-messenger astronomy. The physical information encoded in the GW and EW emissions of an astrophysical object is complementary to each other; in case some physics can not be effectively investigated using the EW channel alone, including the GW channel can be very helpful., Comment: 14 pages, 3 figures; accepted by MNRAS

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 study the co-evolution of supermassive black holes (SMBHs) with galaxies by means of semi-analytic model (SAM) of galaxy formation based on sub-halo merger trees built from Millennium and Millennium-II simulation. We utilize the simulation results from Guo 2013 and Henriques 2015 to study two aspects of the co-evolution, \emph{i.e.} the stochastic gravitational wave (GW) background generated by SMBH merger and the SMBH/galaxy clustering. The characteristic strain amplitude of GW background predicted by Guo 2013 and Henriques 2015 models are $A_{yr^{-1}}=5.00\times10^{-16}$ and $A_{yr^{-1}}=9.42\times10^{-17}$, respectively. We find the GW amplitude is very sensitive to the galaxy merger rate. The difference in the galaxy merger rate between Guo 2013 and Henriques 2015, results in a factor $5$ deviation in the GW strain amplitude. For clusterings, we calculate the spatially isotropic two point auto- and cross-correlation functions (2PCFs) for both SMBHs and galaxies by using the mock catalogs generated from Guo 2013 model. We find that all 2PCFs have positive dependence on both SMBH and galaxy mass. And there exist a significant time evolution in 2PCFs, namely, the clustering effect is enhanced at lower redshifts. Interestingly, this result is not reported in the active galactic nuclei samples in SDSS. Our analysis also shows that, roughly, SMBHs and galaxies, with galaxy mass $10^2\sim10^3$ larger than SMBH mass, have similar pattern of clustering, which is a reflection of the co-evolution of SMBH and galaxy. Finally, we calculate the first ten multiples of the angular power spectrum of the energy density of GW background. We find the amplitude of angular power spectrum of the first ten multiples is about $10\%$ to $60\%$ of the monopole component in the whole frequency range., Comment: 13 pages, 9 figures

We report a tight Hubble constant constraint $67.78^{+1.21}_{-1.86}$ km s$^{-1}$ Mpc$^{-1}$ (2.26\% precision) derived from galaxy redshift surveys. We combine the BAO measurements from 6dFGS, the SDSS DR7 main galaxies, the BOSS DR12 galaxies, and eBOSS DR14 quasars, and also apply the tomographic Alcock-Paczynski (AP) method to the BOSS DR12 galaxies, to place constraints on $H_0$ in the spatially flat $\Lambda$CDM framework. Our result is fully consistent with the CMB constraints from Planck, but in $2.58\sigma$ tension with local measurements of Riess et al. 2016. Compared with the BAO alone constraint, the BAO+AP combined result reduces the error bar by 32%. This shows the strong power of the tomographic AP method in extracting cosmological information from galaxy redshift surveys., Comment: 8 pages, 3 figures, 1 table