Study Of Large-scale Structure Research Articles

Detecting relativistic Doppler by multi-tracing a single galaxy population

New data from ongoing galaxy surveys, such as the Euclid satellite and the Dark Energy Spectroscopic Instrument (DESI), are expected to unveil physics on the largest scales of our universe. Dramatically affected by cosmic variance, these scales are of interest to large-scale structure studies as they exhibit relevant corrections due to general relativity (GR) in the n-point statistics of cosmological random fields. We focus on the relativistic, sample-dependent Doppler contribution to the observed clustering of galaxies, whose detection will further confirm the validity of GR in cosmological regimes. Sample- and scale-dependent, the Doppler term is more likely to be detected via cross-correlation measurements, where it acts as an imaginary correction to the power spectrum of fluctuations in galaxy number counts. We present a method allowing us to exploit multi-tracer benefits from a single data set, by subdividing a galaxy population into two sub-samples, according to galaxies’ luminosity/magnitude. To overcome cosmic variance we rely on a multi-tracer approach, and to maximise the detectability of the relativistic Doppler contribution in the data, we optimise sample selection. As a result, we find the optimal split and forecast the relativistic Doppler detection significance for both a DESI-like Bright Galaxy Sample and a Euclid-like Hα galaxy population.

A theoretical view of the T-web statistical description of the cosmic web

Context. The objective classification of the cosmic web into different environments is an important aspect of large-scale structure studies, as it can be used as a tool to study the formation of structures (halos and galaxies) in mode detail, and it forms a link between their properties and the large-scale environment; these different environments also offer another class of objects whose statistics contain cosmological information. Aims. In this paper, we present an analytical framework to compute the probability of the different environments in the cosmic web based on the so-called T-web formalism, which classifies structures into four different classes (voids, walls, filaments, and knots) based on the eigenvalues of the Hessian of the gravitational potential, often called the tidal tensor. Methods. Our classification method relies on studying whether the eigenvalues of this Hessian matrix are above or below a given threshold and thus requires knowledge of the joint probability distribution of those eigenvalues. We performed a change of variables in terms of rotational invariants, which are polynomials of the field variables and minimally correlated. We studied the distribution of those variables in the linear and quasi-linear regimes with the help of a so-called Gram-Charlier expansion, using tree-order Eulerian perturbation theory to compute the Gram-Charlier coefficients. This expansion then allowed us to predict the probability of the four different environments as a function of the chosen threshold and at a given smoothing scale and redshift for the density field. We checked the validity regime of our predictions by comparing those predictions to measurements made in the N-body Quijote simulations. Results. Working with fields normalised by their linear variance, we find that scaling the threshold value with the non-linear amplitude of fluctuations allows us to capture almost the entire redshift evolution of the probabilities of the four environments, even if we assume that the density field is Gaussian (corresponding to the linear regime of structure formation). We also show that adding mild non-Gaussian corrections with the help of a Gram-Charlier expansion – hence introducing corrections that depend on third-order cumulants of the field – provides even greater accuracy, allowing us to obtain very precise predictions for cosmic web abundances up to scales of as small as ∼5 Mpc h−1 and redshifts down to z ∼ 0.

Simulating emission line galaxies for the next generation of large-scale structure surveys

ABSTRACT We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilize the public code cloudy to compute emission line ratios for a grid of H ii region models. The emission line models assume the same initial mass function as that used to generate the spectral energy distribution of semi-analytical galaxies, ensuring a coherent treatment for modelling the full galaxy spectrum. By incorporating these emission line ratios with galaxy properties, we reproduce observed luminosity functions for H α, H β, [O ii], and [O iii] in the local Universe and at high redshifts. We also find good agreement between model predictions and observations for autocorrelation and cross-correlation functions of [O ii]-selected galaxies, as well as their luminosity dependence. The bias of emission line galaxies depends on both luminosity and redshift. At lower redshifts, it remains constant with increasing luminosity up to around $\sim 10^{42.5}\, {\rm erg\, s^{-1}}$ and then rises steeply for higher luminosities. The transition luminosity increases with redshift and becomes insignificant above z = 1.5. Generally, galaxy bias shows an increasing trend with redshift. However, for luminous galaxies, the bias is higher at low redshifts, as the strong luminosity dependence observed at low redshifts diminishes at higher redshifts. We provide a fitting formula for the bias of emission line galaxies as a function of luminosity and redshift, which can be utilized for large-scale structure studies with future galaxy surveys.

DESI mock challenge: constructing DESI galaxy catalogues based on FastPM simulations

ABSTRACT Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large-scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a halo occupation distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high-resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 emission line galaxy sample down to non-linear scales, i.e. $k\lt 0.5\, h\, \mathrm{Mpc}^{-1}$ or $s\gt 10\, \mathrm{Mpc}\, h^{-1}$. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate (e.g. power spectrum) on the covariance matrix of the Fourier counterpart (e.g. correlation function). We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between 2 and 10 per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.

Numerical simulation and experimental study of quasi-periodic large-scale vortex structures in rod bundle lattices

Study of flow behavior within rod bundles has been an active topic. Surface modification technologies are important parts of the design of the fourth generation reactor, which can increase the strength of the secondary flow within the rod bundle lattices. Quasi-periodic large-scale vortex structure (QLVS) is introduced by arranging micro ribs on the surface of rod bundles, which enhanced the scale of the secondary flow between the rod bundle lattices. Using computational fluid dynamics (CFD) and water experiments, the flow field distribution and drag coefficient of the rod-bundle lattices are studied. The secondary flow between the micro-ribbed rod-bundle lattice is significantly enhanced compared to the standard rod-bundle lattice. The numerical simulation results agree well with the experimental results.

Eliminating polarization leakage effect for neutral hydrogen intensity mapping with deep learning

ABSTRACT The neutral hydrogen (H i) intensity mapping (IM) survey is regarded as a promising approach for cosmic large-scale structure studies. A major issue for the H i IM survey is to remove the bright foreground contamination. A key to successfully removing the bright foreground is to well control or eliminate the instrumental effects. In this work, we consider the instrumental effects of polarization leakage and use the U-Net approach, a deep learning-based foreground removal technique, to eliminate the polarization leakage effect. The thermal noise is assumed to be a subdominant factor compared with the polarization leakage for future H i IM surveys and ignored in this analysis. In this method, the principal component analysis (PCA) foreground subtraction is used as a pre-processing step for the U-Net foreground subtraction. Our results show that the additional U-Net processing could either remove the foreground residual after the conservative PCA subtraction or compensate for the signal loss caused by the aggressive PCA pre-processing. Finally, we test the robustness of the U-Net foreground subtraction technique and show that it is still reliable in the case of existing constraint error on H i fluctuation amplitude.

The FLAMINGO project: cosmological hydrodynamical simulations for large-scale structure and galaxy cluster surveys

ABSTRACT We introduce the Virgo Consortium’s FLAMINGO suite of hydrodynamical simulations for cosmology and galaxy cluster physics. To ensure the simulations are sufficiently realistic for studies of large-scale structure, the subgrid prescriptions for stellar and AGN feedback are calibrated to the observed low-redshift galaxy stellar mass function and cluster gas fractions. The calibration is performed using machine learning, separately for each of FLAMINGO’s three resolutions. This approach enables specification of the model by the observables to which they are calibrated. The calibration accounts for a number of potential observational biases and for random errors in the observed stellar masses. The two most demanding simulations have box sizes of 1.0 and 2.8 Gpc on a side and baryonic particle masses of 1 × 108 and $1\times 10^9\, \text{M}_\odot$, respectively. For the latter resolution, the suite includes 12 model variations in a 1 Gpc box. There are 8 variations at fixed cosmology, including shifts in the stellar mass function and/or the cluster gas fractions to which we calibrate, and two alternative implementations of AGN feedback (thermal or jets). The remaining 4 variations use the unmodified calibration data but different cosmologies, including different neutrino masses. The 2.8 Gpc simulation follows 3 × 1011 particles, making it the largest ever hydrodynamical simulation run to z = 0. Light-cone output is produced on-the-fly for up to 8 different observers. We investigate numerical convergence, show that the simulations reproduce the calibration data, and compare with a number of galaxy, cluster, and large-scale structure observations, finding very good agreement with the data for converged predictions. Finally, by comparing hydrodynamical and ‘dark-matter-only’ simulations, we confirm that baryonic effects can suppress the halo mass function and the matter power spectrum by up to ≈20 per cent.

The halo bispectrum as a sensitive probe of massive neutrinos and baryon physics

ABSTRACT The power spectrum has been a workhorse for cosmological studies of large-scale structure. However, the present-day matter distribution is highly non-Gaussian and significant cosmological information is also contained in higher order correlation functions. Meanwhile, baryon physics (particularly active galactic nucleus feedback) has previously been shown to strongly affect the two-point statistics but there has been limited exploration of its effects on higher order functions to date. Here, we use the bahamas suite of cosmological hydrodynamical simulations to explore the effects of baryon physics and massive neutrinos on the halo bispectrum. In contrast to matter clustering which is suppressed by baryon physics, we find that the halo clustering is typically enhanced. The strength of the effect and the scale over which it extends depends on how haloes are selected. On small scales ($k \gtrsim 1$ h Mpc−1, dominated by satellites of groups/clusters), we find that the bispectrum is highly sensitive to the efficiency of star formation and feedback, making it an excellent testing ground for galaxy formation models. We show that the effects of feedback and the effects of massive neutrinos are largely separable (independent of each other) and that massive neutrinos strongly suppress the halo bispectrum on virtually all scales up to the free-streaming length (apart from the smallest scales, where baryon physics dominates). The strong sensitivity of the bispectrum to neutrinos on the largest scales and galaxy formation physics on the smallest scales bodes well for upcoming precision measurements from the next generation of wide-field surveys.

Seismic analysis of an ancient masonry palace through two simplified numerical methods

The aim of the paper is to study the reliability of simplified numerical models (SNMs) for seismic structural assessment identification accuracy. The advantages of using SNMs with respect to the traditional finite element approaches come from their commercial availability, the structural templates already implemented, and the reduced computational burden that allow the evaluation of the seismic structural assessment in some minutes instead of days. But the drawback to using SNMs alone in the study of large-scale structures is the crude accuracy justified by the simplifications they assume. This study is conducted on an actual case: the Trentacapilli palace (Bisignano, Calabria, Italy). This palace was chosen because it is subjected to foundation subsidence and includes different structural elements, allowing different kinds of SNMs to be used.

Proposals on Modifying Certain Provisions of GOST R 55194-12

Since the putting in force of GOST R 55194 Electrical Equipment and Installations for a.c. Voltages from 1 to 750 kV. General Methods of Dielectric Tests, 10 years have passed. The vast majority of the provisions of GOST R 55194 still remain relevant and do not require changes. At the same time, technical progress has led to the emergence of new knowledge about the physical nature of discharge phenomena and improvement of methods for digital processing of surge voltages. All this made it necessary to modify certain provisions of this regulatory document. The above refers to the procedure for determining corrections to test surge voltages of external insulation and digital processing of lightning voltage surges with superimposed high-frequency oscillations. As a result of the accomplished studies, new formulas filled with physical content have been proposed for determining the parameter q used to determine corrections for atmospheric conditions. Recently obtained new data on experimental studies of large-scale air insulation structures, insulation systems in SF6 and transformer oil have made it possible to propose a well-grounded expression for the test voltage function k(f), used for digital processing of lightning voltage surges with superimposed high-frequency oscillations.

Exploration of 3D wavelet scattering transform coefficients for line-intensity mapping measurements

ABSTRACT The wavelet scattering transform (WST) has recently gained attention in the context of large-scale structure studies, being a possible generator of summary statistics encapsulating non-Gaussianities beyond the reach of the conventional power spectrum. This work examines the three-dimensional solid harmonic WST in the context of a three-dimensional line-intensity mapping measurement to be undertaken by current and proposed phases of the CO Mapping Array Project (COMAP). The WST coefficients demonstrate interpretable behaviour in the context of noiseless CO line-intensity simulations. The contribution of the cosmological z ∼ 3 signal to these coefficients is also detectable in principle even in the Pathfinder phase of COMAP. Using the peak-patch method to generate large numbers of simulations and incorporating observational noise, we numerically estimate covariance matrices and show that careful choices of WST hyperparameters and rescaled or reduced coefficient sets are both necessary to keep covariances well-conditioned. Fisher forecasts show that even a reduced ‘shapeless’ set of ℓ-averaged WST coefficients show constraining power that can exceed that of the power spectrum alone even with similar detection significance. The full WST could improve parameter constraints even over the combination of the power spectrum and the voxel intensity distribution, showing that it uniquely encapsulates shape information about the line-intensity field. However, practical applications urgently require further understanding of the WST in key contexts like covariances and cross-correlations.

Improving cosmological covariance matrices with machine learning

Cosmological covariance matrices are fundamental for parameter inference, since they are responsible for propagating uncertainties from the data down to the model parameters. However, when data vectors are large, in order to estimate accurate and precise covariance matrices we need huge numbers of observations, or rather costly simulations - neither of which may be viable. In this work we propose a machine learning approach to alleviate this problem in the context of the covariance matrices used in the study of large-scale structure. With only a small amount of data (matrices built with samples of 50-200 halo power spectra) we are able to provide significantly improved covariance matrices, which are almost indistinguishable from the ones built from much larger samples (thousands of spectra). In order to perform this task we trained convolutional neural networks to denoise the covariance matrices, using in the training process a data set made up entirely of spectra extracted from simple, inexpensive halo simulations (mocks). We then show that the method not only removes the noise in the covariance matrices of the cheap simulation, but it is also able to successfully denoise the covariance matrices of halo power spectra from N-body simulations. We compare the denoised matrices with the noisy sample covariance matrices using several metrics, and in all of them the denoised matrices score significantly better, without any signs of spurious artifacts. With the help of the Wishart distribution we show that the end product of the denoiser can be compared with an effective sample augmentation in the input matrices. Finally, we show that, by using the denoised covariance matrices, the cosmological parameters can be recovered with nearly the same accuracy as when using covariance matrices built with a sample of 30,000 spectra in the case of the cheap simulations, and with 15,000 spectra in the case of the N-body simulations. Of particular interest is the bias in the Hubble parameter H 0, which was significantly reduced after applying the denoiser.

WISE-PS1-STRM: neural network source classification and photometric redshifts for WISE×PS1

ABSTRACT We cross-match between the WISE All-Sky and PS1 3π DR2 source catalogues. The resulting catalogue has 354 590 570 objects, significantly fewer than the parent PS1 catalogue, but its combination of optical and infrared colours facilitate both better source classification and photometric redshift estimation. We perform a neural network-based classification of the objects into galaxies, quasars, and stars, then run neural network-based photometric redshift estimation for the galaxies. The star sample purity and quasar sample completeness measures improve substantially, and the resulting photo-z’s are significantly more accurate in terms of statistical scatter and bias than those calculated from PS1 properties alone. The catalogue will be a basis for future large-scale structure studies, and will be made available as a high-level science product via the Mikulski Archive for Space Telescopes.

Cosmology behind the mask: Constraining the parameters of ΛCDM with the unmasked galaxy density field from VIPERS

Abstract Galaxy redshift surveys are designed to map cosmic structures in three dimensions for large-scale structure studies. Nevertheless, limitations due to sampling and the survey window are unavoidable and degrade the cosmological constraints. We present an analysis of the VIMOS Public Extragalactic Redshift Survey (VIPERS) over the redshift range 0.6 < z < 1 that is optimised to extract the cosmological parameters while fully accounting for the complex survey geometry. We employ the Gibbs sampling algorithm to iteratively draw samples of the galaxy density field in redshift space, the galaxy bias, the matter density, baryon fraction and growth-rate parameter fσ8 based on a multivariate Gaussian likelihood and prior on the density field. Despite the high number of degrees of freedom, the samples converge to the joint posterior distribution and give self-consistent constraints on the model parameters. We validate the approach using VIPERS mock galaxy catalogues. Although the uncertainty is underestimated by the Gaussian likelihood on the scales that we consider by 50%, the dispersion of the results from the mock catalogues gives a robust error estimate. We find that the precision of the results matches those of the traditional analyses applied to the VIPERS data that use more constrained models. By relaxing the model assumptions, we confirm that the data deliver consistent constraints on the ΛCDM model. This work provides a case-study for the application of maximum-likelihood analyses for the next generation of galaxy redshift surveys.

Modeling the Spectral Diversity of Quasars in the Sixteenth Data Release from the Sloan Digital Sky Survey

Abstract We present a new approach to capturing the broad diversity of emission-line and continuum properties in quasar spectra. We identify populations of spectrally similar quasars through pixel-level clustering on 12,968 high signal-to-noise ratio (S/N) spectra from the Sloan Digital Sky Survey (SDSS) in the redshift range of 1.57 < z < 2.4. Our clustering analysis finds 396 quasar spectra that are not assigned to any population, 15 misclassified spectra, and 6 quasars with incorrect redshifts. We compress the quasar populations into a library of 684 high-S/N composite spectra, anchored in redshift space by the Mg ii emission line. Principal component analysis on the library results in an eigenspectrum basis spanning 1067–4007 Å. We model independent samples of SDSS quasar spectra with the eigenbasis, allowing for a free redshift parameter. Our models achieve a median reduced χ 2 on non–broad absorption line quasar spectra that is reduced by 8.5% relative to models using the eigenspectra from the SDSS spectroscopic pipeline. A significant contribution to the relative improvement is from the ability to reconstruct the range of emission-line variation. The redshift estimates from our model are consistent with the Mg ii emission-line redshift with an average offset that displays 51.4% less redshift-dependent variation relative to the SDSS eigenspectra. Our method for developing quasar spectra models can improve automated classification and predict the intrinsic spectrum in regions affected by intervening absorbers such as Lyα, C iv, and Mg ii, thus benefiting studies of large-scale structure.

DETERMINATION OF GALAXY DISTRIBUTION WITH STATISTICAL MOMENTS

In this report we discuss topological studies of large scale structure of the Universe (LSS) from XMM-Newton, Sloan Digital Sky Survey and simulated data of galaxy distribution. Early works in this mentioned field were based on genus statistics, which is averaged curvature of isosurface of smoothed density field. Later, significant number of other methods was developed. This comprise Euler characteristics, Minkowski functionals, Voronoi clustering, alpha shapes, Delanuay tesselation, Morse theory, Hessian matrix and Soneira-Peebles models. In practice, modern topology methods are reducedto calculation of the three Betti numbers which shall be interpreted as a number of galaxy clusters, filaments and voids. Such an approach was applied by different authors both for simulated and observed LSS data. Topology methods are generally verified using LSS simulations. Observational data normally includes SDSS, CFHTLS and other surveys. These data have many systematical and statistical errors and gaps. Furthermore, there is also a problem of underlying dark matter distribution. The situation is not better in relation to calculations of the power spectrum and its power law index which does not provide a clear picture as well. In this work we propose some tools to solve above problems. First, we performed topology description of simple LSS models such as cubic, graphite-like and random Gaussian distribution of matter. Our next idea is to set a task for LSS topology assessment using X-ray observations of the galaxies. Although, here could be a major complication due to current lack of detected high energy emitting galaxies. Nevertheless, we are expecting to get sufficient results in the future encouraging comprehensive X-ray data. Here we present analysis of statistical moments for four galaxy samples and compare them with the behavior of Betti numbers. Finally, we consider the options of applying artificial neural networks to observed galaxies and fill the data deficiency. This shall enable to define topology at least for superimposed superclusters and other LSS elements.

New horizons in cosmology with spectral distortions of the cosmic microwave background

This Voyage 2050 paper highlights the unique science opportunities using spectral distortions of the cosmic microwave background (CMB). CMB spectral distortions probe many processes throughout the history of the Universe, delivering novel information that complements past, present and future efforts with CMB anisotropy and large-scale structure studies. Precision spectroscopy, possible with existing technology, would not only provide key tests for processes expected within the cosmological standard model but also open an enormous discovery space to new physics. This offers unique scientific opportunities for furthering our understanding of inflation, recombination, reionization and structure formation as well as dark matter and particle physics. A dedicated experimental approach could open this new window to the early Universe in the decades to come, allowing us to turn the long-standing upper distortion limits obtained with COBE/FIRAS some 25 years ago into clear detections of the expected standard distortion signals and also challenge our current understanding of the laws of nature.

Replicability, Repeatability, and Long-term Reproducibility of Cerebellar Morphometry

To identify robust and reproducible methods of cerebellar morphometry that can be used in future large-scale structural MRI studies, we investigated the replicability, repeatability, and long-term reproducibility of three fully automated software tools: FreeSurfer, CEREbellum Segmentation (CERES), and automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization (ACAPULCO). Replicability was defined as computational replicability, determined by comparing two analyses of the same high-resolution MRI data set performed with identical analysis software and computer hardware. Repeatability was determined by comparing the analyses of two MRI scans of the same participant taken during two independent MRI sessions on the same day for the Kirby-21 study. Long-term reproducibility was assessed by analyzing two MRI scans of the same participant in the longitudinal OASIS-2 study. We determined percent difference, the image intraclass correlation coefficient, the coefficient of variation, and the intraclass correlation coefficient between two analyses. Our results show that CERES and ACAPULCO use stochastic algorithms that result in surprisingly high differences between identical analyses for ACAPULCO and small differences for CERES. Changes between two consecutive scans from the Kirby-21 study were less than ± 5% in most cases for FreeSurfer and CERES (i.e., demonstrating high repeatability). As expected, long-term reproducibility was lower than repeatability for all software tools. In summary, CERES is an accurate, as demonstrated before, and reproducible tool for fully automated segmentation and parcellation of the cerebellum. We conclude with recommendations for the assessment of replicability, repeatability, and long-term reproducibility in future studies on cerebellar structure.

Visualization of lncRNA and mRNA Structure Models Within the Integrative Genomics Viewer.

Every class of RNA forms base-paired structures that impact biological functions. Chemical probing of RNA structure, especially with the advent of strategies such as SHAPE-MaP, vastly expands the scale and quantitative accuracy over which RNA structure can be examined. These methods have enabled large-scale structural studies of mRNAs and lncRNAs, but the length and complexity of these RNAs makes interpretation of the data challenging. We have created modules available through the open-source Integrative Genomics Viewer (IGV) for straightforward visualization of RNA structures along with complementary experimental data. Here we present detailed and stepwise strategies for exploring and visualizing complex RNA structures in IGV. Individuals can use these instructions and supplied sample data to become adept at using IGV to visualize RNA structure models in conjunction with useful allied information.

Attention control: The missing link between sensory discrimination and intelligence.

Intelligence is correlated with the ability to make fine sensory discriminations. Although this relationship has been known since the beginning of intelligence testing, the mechanisms underlying this relationship are still unknown. In two large-scale structural equation-modelling studies, we investigated whether individual differences in attention control abilities can explain the relationship between sensory discrimination and intelligence. Across these two studies, we replicated the finding that attention control fully mediated the relationships of intelligence/working-memory capacity to sensory discrimination. Our findings show that attention control plays a prominent role in relating sensory discrimination to higher-order cognitive abilities.