Small-scale Clustering Research Articles

First measurement of the Mg ii forest correlation function in the Epoch of Reionization

Abstract Strong low-ionization transitions like the Mg ii λ2796, 2804Å doublet are believed to produce a detectable ‘metal-line forest’, if metals pollute the neutral IGM. We measure the auto-correlation of the Mg II forest transmission using ten ground based z ≥ 6.80 quasar spectra probing the redshift range $5.96 < z_{\rm Mg\, {\small II}} < 7.42$ ($z_{\rm Mg\, {\small II}, median} = 6.47$). The correlation function exhibits strong small-scale clustering and a pronounced peak at the doublet velocity (Δv = 768 km s−1) arising from discrete absorbers in the CGM of galaxies. After these strong absorbers are identified and masked the signal is consistent with noise. Our measurements are compared to a suite of models generated by combining a large hydrodynamical simulation with a semi-numerical reionization topology, assuming a simple uniform enrichment model. We obtain a 95% credibility upper limit of [Mg/H] < −3.73 at $z_{\rm Mg\, {\small II},median} = 6.47$, assuming uninformative priors on [Mg/H] and the IGM neutral fraction $x_{\rm {H\, {\small I}}}$. Splitting the data into low-z ($5.96 < z_{\rm Mg\, {\small II}} < 6.47$; $z_{\rm Mg\, {\small II},median} = 6.235$) and high-z ($6.47 < z_{\rm Mg\, {\small II}} < 7.42$; $z_{\rm Mg\, {\small II},median} = 6.72$) subsamples again yields null-detections and 95% upper limits of [Mg/H] < −3.75 and [Mg/H] < −3.45, respectively. These first measurements set the stage for making the Mg II forest an emerging tool to precisely constrain the Universe reionization and enrichment history.

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.

A fusion framework to characterize and evaluate air traffic clusters based on potential field theory

The dramatic increase in flight volume and density has inevitably led to the small-scale clustering of aircraft, presenting significant challenges for air traffic controllers due to the complex interrelationships involved. In order to better characterize the clustering phenomenon and its complexity with massive real-time data, this paper introduces a data-driven fusion framework. This framework incorporates an interdependent network and a novel indicator system based on potential field theory to characterize the interaction and superposition effects within these clusters, encompassing aircraft, airspace waypoints, and air traffic controllers. To further explore the complexity of clusters according to the characteristic indicator system, a deep learning model is employed to identify these cluster scenarios. Training results on these scenarios indicate that the deep learning model, using the GRU-attention model, excels in capturing the complex features of clusters. In practical scenario experiments, the proposed fusion framework integrates the interdependent network, characteristic indicator system, and the GRU-attention model. It has proven effective in characterizing clusters and evaluating their complexities. This not only provides real-time alerts but also supports decision-making for air traffic controllers in challenging situations.

Reconstructing the long-wavelength matter density fluctuation modes from the scalar-type clustering fossils

Revealing the large-scale structure from the 21cm intensity mapping surveys is only possible after the foreground cleaning. However, most current cleaning techniques relying on the smoothness of the foreground spectrum lead to a severe side effect of removing the large-scale structure signal along the line of sight. On the other hand, the clustering fossil, a coherent variation of the small-scale clustering over large scales, allows us to recover the long-wavelength density modes from the off-diagonal correlation between short-wavelength modes. In this paper, we revisit the reconstruction based on the short-wavelength matter density modes in real space and scrutinize the requirements for an unbiased and optimal clustering-fossil estimator. We show that (A) the estimator is unbiased only when using an accurate bispectrum model for the long-short-short mode coupling and (B) including the connected four-point correlation functions is essential for characterizing the noise power spectrum of the estimated long mode. For matter in real space, the clustering fossil estimator based upon the leading-order bispectrum yields an unbiased estimation of the long-wavelength (k ≲ 0.01 [h/Mpc]) modes with the cross-correlation coefficient of 0.7 at redshifts z = 0 to 3.

Pulmonary Tuberculosis Notification Rate Within Shenzhen, China, 2010-2019: Spatial-Temporal Analysis.

Pulmonary tuberculosis (PTB) is a chronic communicable disease of major public health and social concern. Although spatial-temporal analysis has been widely used to describe distribution characteristics and transmission patterns, few studies have revealed the changes in the small-scale clustering of PTB at the street level. The aim of this study was to analyze the temporal and spatial distribution characteristics and clusters of PTB at the street level in the Shenzhen municipality of China to provide a reference for PTB prevention and control. Data of reported PTB cases in Shenzhen from January 2010 to December 2019 were extracted from the China Information System for Disease Control and Prevention to describe the epidemiological characteristics. Time-series, spatial-autocorrelation, and spatial-temporal scanning analyses were performed to identify the spatial and temporal patterns and high-risk areas at the street level. A total of 58,122 PTB cases from 2010 to 2019 were notified in Shenzhen. The annual notification rate of PTB decreased significantly from 64.97 per 100,000 population in 2010 to 43.43 per 100,000 population in 2019. PTB cases exhibited seasonal variations with peaks in late spring and summer each year. The PTB notification rate was nonrandomly distributed and spatially clustered with a Moran I value of 0.134 (P=.02). One most-likely cluster and 10 secondary clusters were detected, and the most-likely clustering area was centered at Nanshan Street of Nanshan District covering 6 streets, with the clustering time spanning from January 2010 to November 2012. This study identified seasonal patterns and spatial-temporal clusters of PTB cases at the street level in the Shenzhen municipality of China. Resources should be prioritized to the identified high-risk areas for PTB prevention and control.

An improved halo occupation distribution prescription from UNITsim Hα emission-line galaxies: conformity and modified radial profile

ABSTRACT Emission-line galaxies (ELGs) are targeted by the new generation of spectroscopic surveys to make unprecedented measurements in cosmology from their distribution. Accurately interpreting these data require understanding the imprints imposed by the physics of galaxy formation and evolution on galaxy clustering. In this work, we utilize a semi-analytical model of galaxy formation (sage) to explore the necessary components for accurately reproducing the clustering of ELGs. We focus on developing a halo occupation distribution (HOD) prescription able to reproduce the clustering of sage galaxies. Typically, HOD models assume that satellite and central galaxies of a given type are independent events. We investigate the need for conformity, i.e. whether the average satellite occupation depends on the existence of a central galaxy of a given type. Incorporating conformity into HOD models is crucial for reproducing the clustering in the reference galaxy sample. Another aspect we investigate is the radial distribution of satellite galaxies within haloes. The traditional density profile models, Navarro–Frenk–White (NFW) and Einasto profiles, fail to accurately replicate the small-scale clustering measured for sage satellite galaxies. To overcome this limitation, we propose a generalization of the NFW profile, thereby enhancing our understanding of galaxy clustering.

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of k max = 0.4 Mpc-1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.

Statistical Models for the Dynamics of Heavy Particles in Turbulence

When very small particles are suspended in a fluid in motion, they tend to follow the flow. How such tracer particles are mixed, transported, and dispersed by turbulent flow has been successfully described by statistical models. Heavy particles, with mass densities larger than that of the carrying fluid, can detach from the flow. This results in preferential sampling, small-scale fractal clustering, and large relative velocities. To describe these effects of particle inertia, one must consider both particle positions and velocities in phase space. In recent years, statistical phase-space models have significantly contributed to our understanding of inertial-particle dynamics in turbulence. These models help to identify the key mechanisms and nondimensional parameters governing the particle dynamics and have made qualitative and, in some cases, quantitative predictions. This article reviews statistical phase-space models for the dynamics of small, yet heavy, spherical particles in turbulence. We evaluate their effectiveness by comparing their predictions with results from numerical simulations and laboratory experiments, and we summarize their successes and failures.

The DESI One-Percent Survey: Constructing Galaxy–Halo Connections for ELGs and LRGs Using Auto and Cross Correlations

In the current Dark Energy Spectroscopic Instrument (DESI) survey, emission line galaxies (ELGs) and luminous red galaxies (LRGs) are essential for mapping the dark matter distribution at z ∼ 1. We measure the auto and cross correlation functions of ELGs and LRGs at 0.8 < z ≤ 1.0 from the DESI One-Percent survey. Following Gao et al., we construct the galaxy–halo connections for ELGs and LRGs simultaneously. With the stellar–halo mass relation for the whole galaxy population (i.e., normal galaxies), LRGs can be selected directly by stellar mass, while ELGs can also be selected randomly based on the observed number density of each stellar mass, once the probability P sat of a satellite galaxy becoming an ELG is determined. We demonstrate that the observed small scale clustering prefers a halo mass-dependent P sat model rather than a constant. With this model, we can well reproduce the auto correlations of LRGs and the cross correlations between LRGs and ELGs at r p > 0.1 Mpc h −1. We can also reproduce the auto correlations of ELGs at r p > 0.3 Mpc h −1 (s > 1 Mpc h −1) in real (redshift) space. Although our model has only seven parameters, we show that it can be extended to higher redshifts and reproduces the observed auto correlations of ELGs in the whole range of 0.8 < z ≤ 1.6, which enables us to generate a lightcone ELG mock for DESI. With the above model, we further derive halo occupation distributions for ELGs, which can be used to produce ELG mocks in coarse simulations without resolving subhalos.

Synthetic light-cone catalogues of modern redshift and weak lensing surveys with abacussummit

ABSTRACT The joint analysis of different cosmological probes, such as galaxy clustering and weak lensing, can potentially yield invaluable insights into the nature of the primordial Universe, dark energy, and dark matter. However, the development of high-fidelity theoretical models is a necessary stepping stone. Here, we present public high-resolution weak lensing maps on the light-cone, generated using the N-body simulation suite abacussummit, and accompanying weak lensing mock catalogues, tuned to the Early Data Release small-scale clustering measurements of the Dark Energy Spectroscopic Instrument. Available in this release are maps of the cosmic shear, deflection angle, and convergence fields at source redshifts ranging from z = 0.15 to 2.45 as well as cosmic microwave background convergence maps for each of the 25 base-resolution simulations ($L_{\rm box} = 2000\, h^{-1}\, {\rm Mpc}$ and Npart = 69123) as well as for the two huge simulations ($L_{\rm box} = 7500\, h^{-1}\, {\rm Mpc}$ and Npart = 86403) at the fiducial abacussummit cosmology. The pixel resolution of each map is 0.21 arcmin, corresponding to a healpix Nside of 16 384. The sky coverage of the base simulations is an octant until z ≈ 0.8 (decreasing to about 1800 deg2 at z ≈ 2.4), whereas the huge simulations offer full-sky coverage until z ≈ 2.2. Mock lensing source catalogues are sampled matching the ensemble properties of the Kilo-Degree Survey, Dark Energy Survey, and Hyper Suprime-Cam data sets. The mock catalogues are validated against theoretical predictions for various clustering and lensing statistics, such as correlation multipoles, galaxy–shear, and shear–shear, showing excellent agreement. All products can be downloaded via a Globus endpoint (see Data Availability section).

The MillenniumTNG Project: an improved two-halo model for the galaxy–halo connection of red and blue galaxies

ABSTRACT Approximate methods to populate dark-matter haloes with galaxies are of great utility to galaxy surveys. However, the limitations of simple halo occupation models (HODs) preclude a full use of small-scale galaxy clustering data and call for more sophisticated models. We study two galaxy populations, luminous red galaxies (LRGs) and star-forming emission-line galaxies (ELGs), at two epochs, z = 1 and z = 0, in the large-volume, high-resolution hydrodynamical simulation of the MillenniumTNG project. In a partner study we concentrated on the small-scale, one-halo regime down to r ∼ 0.1 h−1 Mpc, while here we focus on modelling galaxy assembly bias in the two-halo regime, r ≳ 1 h−1 Mpc. Interestingly, the ELG signal exhibits scale dependence out to relatively large scales (r ∼ 20 h−1 Mpc), implying that the linear bias approximation for this tracer is invalid on these scales, contrary to common assumptions. The 10–15 per cent discrepancy is only reconciled when we augment our halo occupation model with a dependence on extrinsic halo properties (‘shear’ being the best-performing one) rather than intrinsic ones (e.g. concentration, peak mass). We argue that this fact constitutes evidence for two-halo galaxy conformity. Including tertiary assembly bias (i.e. a property beyond mass and ‘shear’) is not an essential requirement for reconciling the galaxy assembly bias signal of LRGs, but the combination of external and internal properties is beneficial for recovering ELG the clustering. We find that centrals in low-mass haloes dominate the assembly bias signal of both populations. Finally, we explore the predictions of our model for higher order statistics such as nearest neighbour counts. The latter supplies additional information about galaxy assembly bias and can be used to break degeneracies between halo model parameters.

Small-scale clustering of BOSS galaxies: dependence on luminosity, colour, age, stellar mass, specific star formation rate, and other properties

ABSTRACT We measure and analyse galaxy clustering and the dependence on luminosity, colour, age, stellar mass, and specific star formation rate using Baryon Oscillation Spectroscopic Survey (BOSS) galaxies at 0.48 &amp;lt; z &amp;lt; 0.62. We fit the monopole and quadrupole moments of the two-point correlation function and its projection on scales of 0.1–60.2 h−1 Mpc, after having split the catalogue in a variety of ways. We find that the clustering dependence is consistent with previous well-established results showing the broad trends expected: For example, that brighter, redder, older, more massive and quenched galaxies are more strongly clustered. We also investigate the dependence on additional parameters previously derived from stellar population synthesis model fits to the spectra. We find that galaxy clustering depends on look-back formation time at a low level, while it has little dependence on metallicity. To understand the physics behind these trends, we fit the clustering with a simulation-based emulator to simultaneously model cosmology and galaxy bias using a halo occupation distribution framework. After marginalizing parameters determining the background cosmology, galaxy bias, and a scaling parameter to decouple halo velocity field, we find that the growth rate of large-scale structure as determined by the redshift space distortions is consistent with previous analysis using the full sample, and we do not find evidence that cosmological constraints depend systematically on galaxy selection. This demonstrates that cosmological inference using small-scale clustering measurements is robust to changes in the catalogue selection.

EIGER. II. First Spectroscopic Characterization of the Young Stars and Ionized Gas Associated with Strong Hβ and [O iii] Line Emission in Galaxies at z = 5–7 with JWST

We present emission-line measurements and physical interpretations for a sample of 117 [O iii] emitting galaxies at z = 5.33–6.93, using the first deep JWST/NIRCam wide-field slitless spectroscopic observations. Our 9.7 hr integration is centered upon the z = 6.3 quasar J0100+2802—the first of six fields targeted by the EIGER survey—and covers λ = 3–4 μm. We detect 133 [O iii] doublets, but close pairs motivated by their small scale clustering excess. The galaxies are characterized by a UV luminosity M UV ∼ −19.6 (−17.7 to −22.3), stellar mass ∼108 (106.8−10.1) M ⊙, Hβ and [O iii]4960+5008 EWs ≈ 850 Å (up to 3000 Å), young ages, a highly excited interstellar medium, and low dust attenuations. These high EWs are very rare in the local universe, but we show they are ubiquitous at z ∼ 6 based on the measured number densities. The stacked spectrum reveals Hγ and [O iii]4364, which shows that the galaxies are typically dust- and metal-poor (E (B − V) = 0.1, ) with a high electron temperature (2 × 104 K) and a production efficiency of ionizing photons (ξ ion = 1025.3 Hz erg−1). We further show the existence of a strong mass–metallicity relation. The properties of the stars and gas in z ∼ 6 galaxies conspire to maximize the [O iii] output from galaxies, yielding an [O iii] luminosity density at z ≈ 6 that is significantly higher than that at z ≈ 2. Thus, [O iii] emission-line surveys with JWST prove a highly efficient method to trace the galaxy density in the Epoch of Reionization.

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Halo Model Extensions and Lingering Tension

This paper represents an effort to provide robust constraints on the galaxy–halo connection and simultaneously test the Planck ΛCDM cosmology using a fully numerical model of small-scale galaxy clustering. We explore two extensions to the standard Halo Occupation Distribution model: assembly bias, whereby halo occupation depends on both halo mass and the larger environment, and velocity bias, whereby galaxy velocities do not perfectly trace the velocity of the dark matter within the halo. Moreover, we incorporate halo mass corrections to account for the impact of baryonic physics on the halo population. We identify an optimal set of clustering measurements to constrain this “decorated” HOD model for both low- and high-luminosity galaxies in SDSS DR7. We find that, for low-luminosity galaxies, a model with both assembly bias and velocity bias provides the best fit to the clustering measurements, with no tension remaining in the fit. In this model, we find evidence for both central and satellite galaxy assembly bias at the 99% and 95% confidence levels, respectively. In addition, we find evidence for satellite galaxy velocity bias at the 99.9% confidence level. For high-luminosity galaxies, we find no evidence for either assembly bias or velocity bias, but our model exhibits significant tension with SDSS measurements. We find that all of these conclusions still stand when we include the effects of baryonic physics on the halo mass function, suggesting that the tension we find for high-luminosity galaxies may be due to a problem with our assumed cosmological model.

Halo occupation distribution of Emission Line Galaxies: fitting method with Gaussian processes

The halo occupation distribution (HOD) framework is an empirical method to describe the connection between dark matter halos and galaxies, which is constrained by small scale clustering data. Efficient fitting procedures are required to scan the HOD parameter space. This paper describes such a method based on Gaussian Processes to iteratively build a surrogate model of the posterior of the likelihood surface from a reasonable amount of likelihood computations, typically two orders of magnitude less than standard Monte Carlo Markov chain algorithms. Errors in the likelihood computation due to stochastic HOD modelling are also accounted for in the method we propose. We report results of reproducibility, accuracy and stability tests of the method derived from simulation, taking as a test case star-forming emission line galaxies, which constitute the main tracer of the Dark Energy Spectroscopic Instrument and have so far a poorly constrained galaxy-halo connection from observational data.

The Aemulus Project. V. Cosmological Constraint from Small-scale Clustering of BOSS Galaxies

We analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ∼ 60 h −1 Mpc. Although our simulations are based on wCDM with general relativity (GR), we include a scaling parameter of the halo velocity field, γ f , defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we find f σ 8(z = 0.25) = 0.413 ± 0.031, f σ 8(z = 0.4) = 0.470 ± 0.026, and f σ 8(z = 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1.9σ, 0.3σ, and 3.4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of . We note, however, that the BOSS data is well fit assuming GR, i.e., γ f = 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear scales, but near-future data and modeling will enhance our understanding of the galaxy–halo connection, and provide a strong test of new physics beyond the standard model.

Constraining dark matter decay with cosmic microwave background and weak-lensing shear observations

From observations at low and high redshifts, it is well known that the bulk of dark matter (DM) has to be stable or at least very long-lived. However, the possibility that a small fraction of DM is unstable or that all DM decays with a half-life time (τ) significantly longer than the age of the Universe is not ruled out. One-body decaying dark matter (DDM) consists of a minimal extension to the ΛCDM model. It causes a modification of the cosmic growth history as well as a suppression of the small-scale clustering signal, providing interesting consequences regarding theS8tension, which is the observed difference in the clustering amplitude between weak-lensing (WL) and cosmic microwave background (CMB) observations. In this paper, we investigate models in which a fraction or all DM decays into radiation, focusing on the long-lived regime, that is,τ ≳ H0−1(H0−1being the Hubble time). We used WL data from the Kilo-Degree Survey (KiDS) and CMB data fromPlanck. First, we confirm that this DDM model cannot alleviate theS8difference. We then show that the most constraining power for DM decay does not come from the nonlinear WL data, but from CMB via the integrated Sachs-Wolfe effect. From the CMB data alone, we obtain constraints ofτ ≥ 288 Gyr if all DM is assumed to be unstable, and we show that a maximum fraction off = 0.07 is allowed to decay assuming the half-life time to be comparable to (or shorter than) one Hubble time. The constraints from the KiDS-1000 WL data are significantly weaker,τ ≥ 60 Gyr andf &lt; 0.34. Combining the CMB and WL data does not yield tighter constraints than the CMB alone, except for short half-life times, for which the maximum allowed fraction becomesf = 0.03. All limits are provided at the 95% confidence level.

Full forward model of galaxy clustering statistics with AbacusSummit light cones

ABSTRACT Novel summary statistics beyond the standard 2-point correlation function (2PCF) are necessary to capture the full astrophysical and cosmological information from the small-scale (r &amp;lt; 30h−1Mpc) galaxy clustering. However, the analysis of beyond-2PCF statistics on small scales is challenging because we lack the appropriate treatment of observational systematics for arbitrary summary statistics of the galaxy field. In this paper, we develop a full forward modelling pipeline for a wide range of summary statistics using the large high-fidelity AbacusSummit light cones that account for many systematic effects as well as remain flexible and computationally efficient to enable posterior sampling. We apply our forward model approach to a fully realistic mock galaxy catalog and demonstrate that we can recover unbiased constraints on the underlying galaxy–halo connection model using two separate summary statistics: the standard 2PCF and the novel k-th nearest neighbour (kNN) statistics, which are sensitive to correlation functions of all orders. We will demonstrate its strong constraining power on extended galaxy–halo connection models and cosmology in follow up papers. We expect this to become a powerful approach when applying to upcoming surveys such as DESI where we can leverage a multitude of summary statistics across a wide redshift range to maximally extract information from the non-linear scales.

Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

Cosmological constraints from key probes of theEuclidimaging survey rely critically on the accurate determination of the true redshift distributions,n(z), of tomographic redshift bins. We determine whether the mean redshift, ⟨z⟩, of tenEuclidtomographic redshift bins can be calibrated to theEuclidtarget uncertainties ofσ(⟨z⟩) &lt; 0.002 (1 + z) via cross-correlation, with spectroscopic samples akin to those from the Baryon Oscillation Spectroscopic Survey (BOSS), Dark Energy Spectroscopic Instrument (DESI), andEuclid’s NISP spectroscopic survey. We construct mockEuclidand spectroscopic galaxy samples from the Flagship simulation and measure small-scale clustering redshifts up to redshiftz &lt; 1.8 with an algorithm that performs well on current galaxy survey data. The clustering measurements are then fitted to twon(z) models: one is the truen(z) with a free mean; the other a Gaussian process modified to be restricted to non-negative values. We show that ⟨z⟩ is measured in each tomographic redshift bin to an accuracy of order 0.01 or better. By measuring the clustering redshifts on subsets of the full Flagship area, we construct scaling relations that allow us to extrapolate the method performance to larger sky areas than are currently available in the mock. For the full expectedEuclid, BOSS, and DESI overlap region of approximately 6000 deg2, the uncertainties attainable by clustering redshifts exceeds theEuclidrequirement by at least a factor of three for bothn(z) models considered, although systematic biases limit the accuracy. Clustering redshifts are an extremely effective method for redshift calibration forEuclidif the sources of systematic biases can be determined and removed, or calibrated out with sufficiently realistic simulations. We outline possible future work, in particular an extension to higher redshifts with quasar reference samples.

Covariance matrices for variance-suppressed simulations

ABSTRACT Cosmological N-body simulations provide numerical predictions of the structure of the Universe against which to compare data from ongoing and future surveys, but the growing volume of the Universe mapped by surveys requires correspondingly lower statistical uncertainties in simulations, usually achieved by increasing simulation sizes at the expense of computational power. It was recently proposed to reduce simulation variance without incurring additional computational costs by adopting fixed-amplitude initial conditions. This method has been demonstrated not to introduce bias in various statistics, including the two-point statistics of galaxy samples typically used for extracting cosmological parameters from galaxy redshift survey data, but requires us to revisit current methods for estimating covariance matrices of clustering statistics for simulations. In this work, we find that it is not trivial to construct covariance matrices analytically for fixed-amplitude simulations, but we demonstrate that ezmock (Effective Zel’dovich approximation mock catalogue), the most efficient method for constructing mock catalogues with accurate two- and three-point statistics, provides reasonable covariance matrix estimates for such simulations. We further examine how the variance suppression obtained by amplitude-fixing depends on three-point clustering, small-scale clustering, and galaxy bias, and propose intuitive explanations for the effects we observe based on the ezmock bias model.