Redshift-space Distortions Research Articles

Extracting key information from spectroscopic galaxy surveys

ABSTRACT We develop a novel method to extract key cosmological information, which is primarily carried by the baryon acoustic oscillations (BAO) and redshift space distortions (RSD), from spectroscopic galaxy surveys based on a joint principal component analysis (PCA) and massive optimized parameter estimation and data compression (MOPED) algorithm. We apply this method to galaxy samples from BOSS DR12, and find that a PCA manipulation is effective at extracting the informative modes in the 2D correlation function $\xi (s, \mu)$, giving a tighter constraint on BAO and RSD parameters compared to that using the lowest three multipole moments by the traditional method; i.e. the figure of merit of BAO and RSD parameters is improved by 17 per cent. We then perform a compression of the informative PC modes for BAO and RSD parameters using the MOPED scheme, reducing the dimension of the data vector to the number of interesting parameters, manifesting the joint PCA and MOPED as a powerful tool for clustering analysis with almost no loss of constraining power.

Catalog-level blinding on the bispectrum for DESI-like galaxy surveys

We evaluate the performance of the catalog-level blind analysis technique (blinding) presented in Brieden et al. (2020) in the context of a fixed template power spectrum and bispectrum analysis. This blinding scheme, which is tailored for galaxy redshift surveys similar to the Dark Energy Spectroscopic Instrument (DESI), has two components: the so-called “AP blinding” (concerning the dilation parameters α ∥, α ⊥) and “RSD blinding” (redshift space distortions, affecting the growth rate parameter f). Through extensive testing, including checks for the RSD part in cubic boxes, the impact of AP blinding on mocks with realistic survey sky coverage, and the implementation of a full AP+RSD blinding pipeline, our analysis demonstrates the effectiveness of the technique in preserving the integrity of cosmological parameter estimation when the analysis includes the bispectrum statistic. We emphasize the critical role of sophisticated — and difficult to accidentally unblind — blinding methods in precision cosmology.

Growth of cosmic perturbations in the modified [formula omitted] gravity

We explore the generalized f(R,T) modified theory of gravity, where the gravitational Lagrangian is a function of Ricci scalar R and the trace of the energy–momentum tensor T. We derive modified field equations to the linear order of perturbations in the context of f(R,T) model. We then investigate the growth of perturbations in the context of f(R,T) modified gravity. Primary numerical investigations based on matter power spectra diagrams indicate a structure growth suppression in f(R,T) gravity, which exhibits consistency with local measurements. Also, we notice that matter-geometry interaction in f(R,T) model would results in the specific feature named as ”matter acoustic oscillations” appeared in matter power spectra diagrams. Moreover, we put constraints on the cosmological parameters of f(R,T) model, utilizing current observations, chiefly cosmic microwave background (CMB), weak lensing, supernovae, baryon acoustic oscillations, and redshift-space distortions data. Numerical results based on MCMC calculations imply that f(R,T) is a qualified theory of modified gravity in reconciling Planck CMB data with local probes of large scale structures, by reporting lower values for the structure growth parameter σ8 compared to the standard model of cosmology.

Probing dark energy evolution post-DESI 2024

We study the evidence for dark energy (DE) evolution at low redshift, using baryonic acoustic oscillations (BAOs) from the DESI Early Data Release, Pantheon+ Type Ia supernovae (SNe-Ia), and redshift space distortions (RSDs) to constrain cosmological parameters. Furthermore, we make use of the angular acoustic scale to analyse the effect of introducing condensed CMB information on the cosmological parameters informing DE evolution. The analysis is divided into cases based on the variability of priors inferred from early-time physics. Using a quadratic parametrisation, X(z), for DE density, we find evidence for DE evolution in all cases, both with and without the angular acoustic scale. We reconstruct X(z) using best fit parameters and find that DE density starts to exhibit dynamical behaviour at z∼0.5, assuming negative values beyond z∼1.5. The data show no significant preference for X(z)CDM over a ΛCDM, with both models performing equally well according to our chosen metrics of reduced χ2 and the Durbin–Watson statistic.

Redshift-space distortions corner interacting dark energy

Despite the fact that the ΛCDM model has been highly successful over the last few decades in providing an accurate fit to a broad range of cosmological and astrophysical observations, different intriguing tensions and anomalies emerged at various statistical levels. Given the fact that the dark energy and the dark matter sectors remain unexplored, the answer to some of the tensions may rely on modifications of these two dark sectors. This manuscript explores the important role of the growth of structure in constraining non-standard cosmologies. In particular, we focus on the interacting dark energy (IDE) scenario, where dark matter and dark energy interact non-gravitationally. We aim to place constraints on the phenomenological parameters of these alternative models, by considering different datasets related to a number of cosmological measurements, to achieve a complementary analysis. A special emphasis is devoted to redshift-space distortion measurements (RSD), whose role in constraining beyond the standard paradigm models has not been recently highlighted. These observations indeed have a strong constraining power, rendering all parameters to their ΛCDM canonical values, and therefore leaving little room for the IDE models explored here.

Analysis of an iterative reconstruction method in comparison of the standard reconstruction method

ABSTRACT We present a detailed analysis of a new iterative density reconstruction algorithm. This algorithm uses a decreasing smoothing scale to better reconstruct the density field in Lagrangian space. We implement this algorithm to run on the quijote simulations, and extend it to (a) include a smoothing kernel that smoothly goes from anisotropic to isotropic, and (b) a variant that does not correct for redshift space distortions. We compare the performance of this algorithm with the standard reconstruction method. Our examinations of the methods include cross-correlation of the reconstructed density field with the linear density field, reconstructed two-point functions, and BAO parameter fitting. We also examine the impact of various parameters, such as smoothing scale, anisotropic smoothing, tracer type/bias, and the inclusion of second order perturbation theory. We find that the two reconstruction algorithms are comparable in most of the areas we examine. In particular, both algorithms give consistent fittings of BAO parameters. The fits are robust over a range of smoothing scales. We find the iterative algorithm is significantly better at removing redshift space distortions. The new algorithm will be a promising method to be employed in the ongoing and future large-scale structure surveys.

Apparent Parity Violation in the Observed Galaxy Trispectrum.

Recent measurements of the four-point correlation function in large-scale galaxy surveys have found apparent evidence of parity violation in the distribution of galaxies. This cannot happen via dynamical gravitational effects in general relativity. If such a violation arose from physics in the early Universe it could indicate important new physics beyond the standard model, and would be at odds with most models of inflation. It is therefore now timely to consider the galaxy trispectrum in more detail. While the intrinsic four-point correlation function, or equivalently the trispectrum, its Fourier counterpart, is parity invariant, the observed trispectrum must take redshift-space distortions into account. Although the standard Newtonian correction also respects parity invariance, we show that subleading relativistic corrections do not. We demonstrate that these can be significant at intermediate linear scales and are dominant over the Newtonian parity-invariant part around the equality scale and above. Therefore when observing the galaxy four-point correlation function, we should expect to detect parity violation on large scales.

Neural network reconstruction of density and velocity fields from the 2MASS Redshift Survey

Aims. Our aim is to reconstruct the 3D matter density and peculiar velocity fields in the local Universe up to a distance of 200 h−1 Mpc from the Two-Micron All-Sky Redshift Survey (2MRS) using a neural network (NN). Methods. We employed an NN with a U-net autoencoder architecture and a weighted mean squared error loss function trained separately to output either the density or velocity field for a given input grid of galaxy number counts. The NN was trained on mocks derived from the Quijote N-body simulations, incorporating redshift-space distortions (RSDs), galaxy bias, and selection effects closely mimicking the characteristics of 2MRS. The trained NN was benchmarked against a standard Wiener filter (WF) on a validation set of mocks before applying it to 2MRS. Results. The NN reconstructions effectively approximate the mean posterior estimate of the true density and velocity fields conditioned on the observations. They consistently outperform the WF in terms of reconstruction accuracy and effectively capture the nonlinear relation between velocity and density. The NN-reconstructed bulk flow of the total survey volume exhibits a significant correlation with the true mock bulk flow, demonstrating that the NN is sensitive to information on “super-survey” scales encoded in the RSDs. When applied to 2MRS, the NN successfully recovers the main known clusters, some of which are partially in the Zone of Avoidance. The reconstructed bulk flows in spheres of different radii less than 100 h−1 Mpc are in good agreement with a previous 2MRS analysis that required an additional external bulk flow component inferred from directly observed peculiar velocities. The NN-reconstructed peculiar velocity of the Local Group closely matches the observed Cosmic Microwave Background dipole in amplitude and Galactic latitude, and only deviates by 18° in longitude. The NN-reconstructed fields are publicly available.

Revisiting the effects of baryon physics on small-scale redshift space distortions

Abstract Redshift space distortions are an important probe of the growth of large-scale structure and for constraining cosmological parameters in general. As galaxy redshift surveys approach percent level precision in their observations of the two point clustering statistics, it is timely to review what effects baryons and associated processes such as feedback may have on small-scale clustering in redshift space. Contrary to previous studies in the literature, we show using the large-volume Bahamas hydrodynamic simulations that the effect of baryons can be as much as 1 per cent in the k ∼ 0.1 h Mpc−1 range for the monopole and 5 per cent for quadrupole, and that this could rise to as much as 10 per cent at k ∼ 10 h Mpc−1 in both measurements. For the halo power spectra, this difference can be as much 3-4 per cent in the monopole on scales of 0.05 < k < 0.3 h Mpc−1 for 1013 h−1 M⊙ haloes. We find that these deviations can be mitigated to the sub-percent level in the both the monopole and quadrupole up to k ∼ 0.3 h Mpc−1 if the baryon corrected halo masses are used to calculate the redshift space power spectra. Finally, we use the cosmo-OWLS simulation suite to explore the changes in the redshift space power spectra with different feedback prescriptions, finding that there is a maximum of 15-20 per cent difference between the redshift space monopole and quadrupole with and without baryons at k ∼ 1–2 h Mpc−1 within these models.

Enhancing Morphological Measurements of the Cosmic Web with Delaunay Tessellation Field Estimation

The density fields constructed by traditional mass assignment methods are susceptible to irritating discreteness, which hinders morphological measurements of cosmic large-scale structure (LSS) through Minkowski functionals (MFs). To alleviate this issue, fixed-kernel smoothing methods are commonly used in the literature, at the expense of losing substantial structural information. In this work, we propose to measure MFs with the Delaunay tessellation field estimation (DTFE) technique, with the goal of maximizing the extraction of morphological information from sparse tracers. We perform our analyses starting from matter fields and progressively extending to halo fields. At the matter-field level, we elucidate how discreteness affects morphological measurements of LSS. Then, by comparing with the traditional Gaussian smoothing scheme, we preliminarily showcase the advantages of DTFE for enhancing measurements of MFs from sparse tracers. At the halo-field level, we first numerically investigate various systematic effects on MFs of DTFE fields, which are induced by finite voxel sizes, halo number densities, halo weightings, and redshift space distortions (RSDs), respectively. Then, we explore the statistical power of MFs measured with DTFE for extracting the cosmological information encoded in RSDs. We find that MFs measured with DTFE exhibit improvements by ∼2 orders of magnitude in discriminative power for RSD effects and by a factor of ∼3–5 in constraining power on the structure growth rate over the MFs measured with Gaussian smoothing. These findings demonstrate the remarkable enhancements in statistical power of MFs achieved by DTFE, showing enormous application potentials for our method in extracting various key cosmological information from galaxy surveys.

Estimation of line-of-sight velocities of individual galaxies using neural networks – I. Modelling redshift–space distortions at large scales

ABSTRACT We present a scheme based on artificial neural networks (ANNs) to estimate the line-of-sight velocities of individual galaxies from an observed redshift–space galaxy distribution. We find an estimate of the peculiar velocity at a galaxy based on galaxy counts and barycentres in shells around it. By training the network with environmental characteristics, such as the total mass and mass centre within each shell surrounding every galaxy in redshift space, our ANN model can accurately predict the line-of-sight velocity of each individual galaxy. When this velocity is used to eliminate the RSD effect, the two-point correlation function (TPCF) in real space can be recovered with an accuracy better than 1 per cent at s > 8 $\, h^{-1}\, \mathrm{Mpc}$, and 4 per cent on all scales compared to ground truth. The real-space power spectrum can be recovered within 3 per cent on k< 0.5 $\, \mathrm{Mpc}^{-1}\, h$, and less than 5 per cent for all k modes. The quadrupole moment of the TPCF or power spectrum is almost zero down to s = 10 $\, h^{-1}\, \mathrm{Mpc}$ or all k modes, indicating an effective correction of the spatial anisotropy caused by the RSD effect. We demonstrate that on large scales, without additional training with new data, our network is adaptable to different galaxy formation models, different cosmological models, and mock galaxy samples at high-redshifts and high biases, achieving less than 10 per cent error for scales greater than 15 $\, h^{-1}\, \mathrm{Mpc}$. As it is sensitive to large-scale densities, it does not manage to remove Fingers of God in large clusters, but works remarkably well at recovering real-space galaxy positions elsewhere. Our scheme provides a novel way to predict the peculiar velocity of individual galaxies, to eliminate the RSD effect directly in future large galaxy surveys, and to reconstruct the three-dimensional cosmic velocity field accurately.

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We search for a redshift dependence of σ80\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _8^0$$\\end{document} using 23 growth rate measurements from redshift space distortions and peculiar velocity measurements as a consistency check of Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM. For this purpose we use the dataset from Sagredo et al. (Phys Rev D 98:083543, 2018) consisting of 22 measurements, which has been vetted for internal consistencies. Eighteen of these measurements have been obtained from the Gold-2017 sample, collated from multiple redshift space distortion surveys between 2009 and 2016, whereas the remaining four have been obtained from the eBOSS DR14 quasar survey. We also added one additional data point from the BOSS DR12 CMASS galaxy sample. We find that for this dataset the σ80\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _8^0$$\\end{document} values are consistent between the low redshift and high redshift samples using three different redshift cuts. This implies that the growth rate measurements are consistent with a constant σ80\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma _8^0$$\\end{document} in accord with Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM, assuming there are no uncontrolled systematics in this dataset.

Constraining holographic dark energy and analyzing cosmological tensions

We investigate cosmological constraints on the holographic dark energy (HDE) using the state-of-the-art cosmological datasets: Planck CMB angular power spectra and weak lensing power spectra, Atacama Cosmology Telescope (ACT) temperature power spectra, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) measurements from six-degree-field galaxy survey and Sloan Digital Sky Survey (DR12 & DR16) and the Cepheids-Supernovae measurement from SH0ES team (R22). We also examine the HDE model and ΛCDM with and without Neff (effective number of relativistic species) being treated as a free parameter. We find that the HDE model can relieve the tensions of H0 and S8 to certain degrees. With “Planck+ACT+BAO+RSD” datasets, the constraints are H0=69.70±1.39km s−1Mpc−1 and S8=0.823±0.011 in HDE model, which brings down the Hubble tension down to 1.92σ confidence level (C.L.) and the S8 tension to (1−2)σ C.L. By adding the R22 data, their values are improved as H0=71.86±0.93km s−1Mpc−1 and S8=0.813±0.010, which further brings the Hubble tension down to 0.85σ C.L. and relieves the S8 tension. We also quantify the goodness-of-fit of different models with Akaike information criterion (AIC) and Bayesian information criterion (BIC), and find that the HDE agrees with the observational data better than the ΛCDM and other extended models (treating Neff as free for fitting).

CosmoMIA: cosmic web-based redshift space halo distribution

Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive understanding of the Universe.In this research, we explore sub-grid modeling to create accurate catalogs, beginning with coarse grid number counts at resolutions of approximately 5.5 h -1 Mpc per side. These resolutions strike a balance between modeling nonlinear damping of baryon acoustic oscillations and facilitating large-volume simulations. Augmented Lagrangian Perturbation Theory (ALPT) is utilized to model the dark matter field and motions, replicating the clustering of a halo catalog derived from a massive simulation at z = 1.1.Our approach involves four key stages: Tracer Assignment: Allocating dark matter particles to tracers based on grid cell counts, generating additional particles to address discrepancies. Attractor Identification: Defining attractors based on particle cosmic web environments, acting as gravitational focal points. Tracer Collapse: Guiding tracers towards attractors, simulating structure collapse. Redshift Space Distortions: Introducing redshift space distortions to simulated catalogs using ALPT and a random dispersion term. Results demonstrate accurate reproduction of monopoles and quadrupoles up to wave numbers of approximately k = 0.6 h Mpc-1. This method holds significant promise for galaxy surveys like DESI, EUCLID, and LSST, enhancing our understanding of the cosmos across scales.

Peculiar Velocity Reconstruction from Simulations and Observations Using Deep Learning Algorithms

In this paper, we introduce a U-Net model of deep learning algorithms for reconstructions of the 3D peculiar velocity field, which simplifies the reconstruction process with enhanced precision. We test the adaptability of the U-Net model with simulation data under more realistic conditions, including the redshift space distortion effect and halo mass threshold. Our results show that the U-Net model outperforms the analytical method that runs under ideal conditions, with a 16% improvement in precision, 13% in residuals, 18% in correlation coefficient, and 27% in average coherence. The deep learning algorithm exhibits exceptional capacities to capture velocity features in nonlinear regions and substantially improve reconstruction precision in boundary regions. We then apply the U-Net model trained under Sloan Digital Sky Survey (SDSS) observational conditions to the SDSS Data Release 7 data for observational 3D peculiar velocity reconstructions.

CLASS-OneLoop: accurate and unbiased inference from spectroscopic galaxy surveys

The power spectrum is the most commonly applied summary statistics to extract cosmological information from the observed three-dimensional distribution of galaxies in spectroscopic surveys. We present CLASS-OneLoop, a new numerical tool, fully integrated into the Boltzmann code CLASS, enabling the calculation of the one-loop power spectrum of biased tracers in spectroscopic surveys. Built upon the Eulerian moment expansion framework for redshift-space distortions, the implemented model incorporates a complete set of nonlinear biases, counterterms, and stochastic contributions, and includes the infrared resummation and the Alcock-Paczynski effect. The code features an evaluation of the loops by either direct numerical integration or Fast Fourier Transform, and employs a fast-slow parameter decomposition, which is essential for accelerating MCMC runs. After presenting performance and validation tests, as an illustration of the capabilities of the code, we apply it to fit the measured redshift-space halo power spectrum wedges on a ΛCDM subset of the AbacusSummit simulation suite and considering scales up to kmax = 0.3 h/Mpc. We find that the one-loop model adeptly recovers the fiducial cosmology of the simulation, while a simplified model commonly used in the literature for sensitivity forecasts yields significantly biased results. Furthermore, we conduct Monte Carlo Markov Chain (MCMC) forecasts for a DESI-like survey, considering a model with a dynamical dark energy component. Our results demonstrate the ability to independently constrain cosmological and nuisance parameters, even in the presence of a large parameter space with twenty-nine variables.

Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-LCDM models

The space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the Lambda CDM model. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali--Gabadadze--Porrati model, the Hu--Sawicki $f(R)$ model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein--de Sitter universe; we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos; we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya--Nishimishi--Saito prescription and the effective field theory of large-scale structure. This work provides a first validation of the various codes being considered by for the spectroscopic clustering probe in beyond-Lambda CDM scenarios. We calculated and compared the $ statistic to assess the different modelling choices. This was done by fitting the spectroscopic clustering predictions to measurements from numerical simulations and perturbation theory-based mock data. We compared the behaviour of this statistic in the beyond-Lambda CDM cases, as a function of the maximum scale included in the fit, to the baseline Lambda CDM case. We find that the Einstein--de Sitter approximation without screening is surprisingly accurate for the modified gravity cases when comparing to the halo clustering monopole and quadrupole obtained from simulations and mock data. Further, we find the same goodness-of-fit for both cases -- the one including and the one omitting non-standard physics in the predictions. Our results suggest that the inclusion of multiple redshift bins, higher-order multipoles, higher-order clustering statistics (such as the bispectrum), and photometric probes such as weak lensing, will be essential to extract information on massive neutrinos, modified gravity and dark energy. Additionally, we show that the three codes used in our analysis, namely PBJ Pybird and MG-Copter exhibit sub-percent agreement for $k Mpc $ across all the models. This consistency underscores their value as reliable tools.

High-accuracy emulators for observables in ΛCDM, Neff, Σmν, and w cosmologies

ABSTRACT We use the emulation framework CosmoPower to construct and publicly release neural network emulators of cosmological observables, including the cosmic microwave background (CMB) temperature and polarization power spectra, matter power spectrum, distance-redshift relation, baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) observables, and derived parameters. We train our emulators on Einstein–Boltzmann calculations obtained with high-precision numerical convergence settings, for a wide range of cosmological models including ΛCDM, wCDM, ΛCDM + Neff, and ΛCDM + Σmν. Our CMB emulators are accurate to better than 0.5 per cent out to ℓ = 104, which is sufficient for Stage-IV data analysis, and our P(k) emulators reach the same accuracy level out to $k=50 \, \, \mathrm{Mpc}^{-1}$, which is sufficient for Stage-III data analysis. We release the emulators via an online repository (CosmoPower Organisation), which will be continually updated with additional extended cosmological models. Our emulators accelerate cosmological data analysis by orders of magnitude, enabling cosmological parameter extraction analyses, using current survey data, to be performed on a laptop. We validate our emulators by comparing them to class and camb and by reproducing cosmological parameter constraints derived from Planck TT, TE, EE, and CMB lensing data, as well as from the Atacama Cosmology Telescope Data Release 4 CMB data, Dark Energy Survey Year-1 galaxy lensing and clustering data, and Baryon Oscillation Spectroscopic Survey Data Release 12 BAO and RSD data.

Gravitational redshift constraints on the effective theory of interacting dark energy

Upcoming galaxy surveys provide the necessary sensitivity to measure gravitational redshift, a general relativistic effect that generates a dipole in galaxy clustering data when correlating two distinct populations of galaxies. Here, we study the constraining power of gravitational redshift within the framework of the effective theory of interacting dark energy. This formalism describes linear cosmological perturbations in scalar-tensor theories of gravity with a limited number of free functions, and allows each particle species to be coupled differently to the gravitational sector. In this work, we focus on Horndeski theories with a non-minimal coupling of dark matter to the scalar degree of freedom, yielding a breaking of the weak equivalence principle for this cosmic component, a scenario that is yet untested. We show that the dipole generated by gravitational redshift significantly breaks degeneracies and tightens the constraints on the parameters of the effective theory compared to the standard redshift-space distortion analysis solely based on the even multipoles in the galaxy correlation function, with an improvement of up to ∼ 50% for populations with a galaxy bias difference equal to 1. We make the Python package EF-TIGRE (Effective Field Theory of Interacting dark energy with Gravitational REdshift) developed for this work publicly available (https://github.com/Mik3M4n/EF-TIGRE).

Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample

ABSTRACT We present a clustering analysis of the BOSS DR12 CMASS galaxy sample, combining measurements of the galaxy two-point correlation function and density-split clustering down to a scale of $1 \, h^{-1}\, \text{Mpc}$. Our theoretical framework is based on emulators trained on high-fidelity mock galaxy catalogues that forward model the cosmological dependence of the clustering statistics within an extended-ΛCDM framework, including redshift-space and Alcock–Paczynski distortions. Our base-ΛCDM analysis finds ωcdm = 0.1201 ± 0.0022, σ8 = 0.792 ± 0.034, and ns = 0.970 ± 0.018, corresponding to fσ8 = 0.462 ± 0.020 at z ≈ 0.525, which is in agreement with Planck 2018 predictions and various clustering studies in the literature. We test single-parameter extensions to base-ΛCDM, varying the running of the spectral index, the dark energy equation of state, and the density of mass-less relic neutrinos, finding no compelling evidence for deviations from the base model. We model the galaxy–halo connection using a halo occupation distribution framework, finding signatures of environment-based assembly bias in the data. We validate our pipeline against mock catalogues that match the clustering and selection properties of CMASS, showing that we can recover unbiased cosmological constraints even with a volume 84 times larger than the one used in this study.