Mock Galaxy Research Articles

Secondary halo bias through cosmic time II. Reconstructing halo properties using clustering information

When constructing mock galaxy catalogs based on suites of dark matter halo catalogs generated with approximated, calibrated, or machine-learning approaches, assigning intrinsic properties for these tracers is a step of paramount importance, given that they can shape the abundance and spatial distribution of mock galaxies and galaxy clusters. We explore the possibility of assigning properties of dark matter halos within the context of calibrated or learning approaches, explicitly using clustering information. The goal is to retrieve the correct signal of primary and secondary large-scale effective bias as a function of properties reconstructed solely based on phase-space properties of the halo distribution and dark matter density field. The algorithm reconstructs a set of halo properties (such as virial mass, maximum circular velocity, concentration, and spin) constrained to reproduce both primary and secondary (or assembly) bias. The key ingredients of the algorithm are the implementation of individually-assigned large-scale effective bias, a multi-scale approach to account for halo exclusion, and a hierarchical assignment of halo properties. The method facilitates the assignment of halo properties, aiming to replicate the large-scale effective bias, both primary and secondary. This constitutes an improvement over previous methods in the literature, especially for the high-mass end population. We have designed a strategy for reconstructing the main properties of dark matter halos obtained using calibrated or learning algorithms, such that the one- and two-point statistics (on large scales) replicate the signal from detailed $N$-body simulations. We encourage the application of this strategy (or the implementation of our algorithm) for the generation of mock catalogs of dark matter halos based on approximated methods.

Inferring Cosmological Parameters on SDSS via Domain-generalized Neural Networks and Light-cone Simulations

We present a proof-of-concept simulation-based inference on Ωm and σ 8 from the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey (BOSS) LOWZ Northern Galactic Cap (NGC) catalog using neural networks and domain generalization techniques without the need of summary statistics. Using rapid light-cone simulations L-picola, mock galaxy catalogs are produced that fully incorporate the observational effects. The collection of galaxies is fed as input to a point cloud-based network, Minkowski-PointNet . We also add relatively more accurate Gadget mocks to obtain robust and generalizable neural networks. By explicitly learning the representations that reduce the discrepancies between the two different data sets via the semantic alignment loss term, we show that the latent space configuration aligns into a single plane in which the two cosmological parameters form clear axes. Consequently, during inference, the SDSS BOSS LOWZ NGC catalog maps onto the plane, demonstrating effective generalization and improving prediction accuracy compared to non-generalized models. Results from the ensemble of 25 independently trained machines find Ωm = 0.339 ± 0.056 and σ 8 = 0.801 ± 0.061, inferred only from the distribution of galaxies in the light-cone slices without relying on any indirect summary statistics. A single machine that best adapts to the Gadget mocks yields a tighter prediction of Ωm = 0.282 ± 0.014 and σ 8 = 0.786 ± 0.036. We emphasize that adaptation across multiple domains can enhance the robustness of the neural networks in observational data.

Detection of the large-scale tidal field with galaxy multiplet alignment in the DESI Y1 spectroscopic survey

ABSTRACT We explore correlations between the orientations of small galaxy groups, or ‘multiplets’, and the large-scale gravitational tidal field. Using data from the Dark Energy Spectroscopic Instrument (DESI) Y1 survey, we detect the intrinsic alignment (IA) of multiplets to the galaxy-traced matter field out to separations of $100\,h^{-1}$ Mpc. Unlike traditional IA measurements of individual galaxies, this estimator is not limited by imaging of galaxy shapes and allows for direct IA detection beyond redshift $z=1$. Multiplet alignment is a form of higher order clustering, for which the scale-dependence traces the underlying tidal field and amplitude is a result of small-scale ($\lt 1h^{-1}$ Mpc) dynamics. Within samples of bright galaxies, luminous red galaxies (LRG) and emission-line galaxies, we find similar scale-dependence regardless of intrinsic luminosity or colour. This is promising for measuring tidal alignment in galaxy samples that typically display no IA. DESI’s LRG mock galaxy catalogues created from the A bacusS ummitN-body simulations produce a similar alignment signal, though with a 33 per cent lower amplitude at all scales. An analytic model using a non-linear power spectrum (NLA) only matches the signal down to 20 $h^{-1}$ Mpc. Our detection demonstrates that galaxy clustering in the non-linear regime of structure formation preserves an interpretable memory of the large-scale tidal field. Multiplet alignment complements traditional two-point measurements by retaining directional information imprinted by tidal forces, and contains additional line-of-sight information compared to weak lensing. This is a more effective estimator than the alignment of individual galaxies in dense, blue, or faint galaxy samples.

The Reliability of Type Ia Supernovae Delay-time Distributions Recovered from Galaxy Star Formation Histories

We present a numerical analysis investigating the reliability of Type Ia supernova (SN Ia) delay-time distributions recovered from individual host galaxy star formation histories. We utilize star formation histories of mock samples of galaxies generated from the IllustrisTNG simulation at two redshifts to recover delay-time distributions. The delay-time distributions are constructed through piecewise constants as opposed to typically employed parametric forms such as power laws or Gaussian or skew/lognormal functions. The SN Ia delay-time distributions are recovered through a Markov Chain Monte Carlo exploration of the likelihood space by comparing the expected SN Ia rate within each mock galaxy to the observed rate. We show that a reduced representative sample of nonhost galaxies is sufficient to reliably recover delay-time distributions while simultaneously reducing the computational load. We also highlight a potential systematic between recovered delay-time distributions and the mass-weighted ages of the underlying host galaxy stellar population.

Inferring the Mass Content of Galaxy Clusters with Satellite Kinematics and Jeans Anisotropic Modeling

Satellite galaxies can be used to indicate the dynamic mass of galaxy groups and clusters. In this study, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion (JAM) modeling to satellite galaxies in 28 galaxy clusters selected from the TNG300-1 simulation with halo masses of log10M200/M⊙>14.3 . If using true bound satellites as tracers, the best constrained total mass within the half-mass radius of satellites, M(<r half), and the virial mass, M 200, have average biases of −0.01 and 0.03 dex, with average scatters of 0.11 dex and 0.15 dex. If selecting companions in the redshift space with a line-of-sight depth of 2000 km s−1, the biases are −0.06 and 0.01 dex, while the scatters are 0.12 and 0.18 dex for M(<r half) and M 200. By comparing the best-fitting and actual density profiles, we find that ∼29% of the best-fitting density profiles show very good agreement with the truth, ∼32% display over/underestimates at most of the radial range with biased M(<r half), and 39% show under/overestimates in central regions and over/underestimates in the outskirts, with good constraints on M(<r half); yet most of the best constraints are still consistent with the true profiles within 1σ statistical uncertainties for the three circumstances. Using a mock DESI Bright Galaxy Survey catalog with the effect of fiber incompleteness, we find DESI fiber assignments and the choice of flux limits barely modify the velocity dispersion profiles and are thus unlikely to affect the dynamical modeling outcomes. Our results show that with current and future deep spectroscopic surveys, JAM can be a powerful tool to constrain the underlying density profiles of individual massive galaxy clusters.

Counting the Unseen. I. Nuclear Density Scaling Relations for Nucleated Galaxies

The volumetric rate of tidal disruption events (TDEs) encodes information on the still-unknown demographics of central massive black holes (MBHs) in low-mass galaxies (≲109 M ⊙). Theoretical TDE rates from model galaxy samples can extract this information, but this requires accurately defining the nuclear stellar density structures. This region is typically dominated by nuclear star clusters (NSCs), which have been shown to increase TDE rates by orders of magnitude. Thus, we assemble the largest available sample of parsec-scale 3D density profiles that include NSC components. We deproject the point-spread-function-deconvolved surface-brightness profiles of 91 nearby galaxies of varying morphology and combine these with nuclear mass-to-light ratios estimated from measured colors or spectral synthesis to create 3D mass density profiles. We fit the inner 3D density profile to find the best-fit power-law density profile in each galaxy. We compile this information as a function of galaxy stellar mass to fit new empirical density scaling relations. These fits reveal positive correlations between galaxy stellar mass and central stellar density in both early- and late-type galaxies. We find that early-type galaxies have somewhat higher densities and shallower profiles relative to late-type galaxies at the same mass. We also use the density profiles to estimate the influence radius of each galaxy’s MBH and find that the sphere of influence was likely resolved in most cases. These new relations will be used in future works to build mock galaxy samples for dynamical TDE rate calculations, with the aim of constraining MBH demographics in low-mass galaxies.

Measuring the Conditional Luminosity and Stellar Mass Functions of Galaxies by Combining the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 Data

In this investigation, we leverage the combination of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 data sets to estimate the conditional luminosity functions and conditional stellar mass functions (CLFs and CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI mock galaxy redshift survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r- and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low-redshift bin at the low-luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift, the faint-end slopes of CLFs and CSMFs below ∼109 h −2 L ⊙ (or h −2 M ⊙) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following Chen et al., the faint-end slopes of the LFs/SMFs turn out to also be in good agreement with the slope of the halo mass function.

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 &amp;gt; 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&amp;lt; 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.

The cosmic microwave background lensing imprint of cosmic voids detected in the WISE-Pan-STARRS luminous red galaxy catalog

The cross-correlation of cosmic voids with the lensing convergence (kappa ) map of cosmic microwave background (CMB) fluctuations offers a powerful tool with which to refine our understanding of the dark sector in the consensus cosmological model. Our principal aim is to compare the lensing signature of our galaxy dataset with simulations based on the concordance model and to characterize the results with an $A_ kappa $ consistency parameter normalized to unity. In particular, our measurements contribute to the understanding of the “lensing-is-low” tension of the Lambda CDM model. In this analysis, we selected luminous red galaxies (LRGs) from the WISE- Pan-STARRS dataset, enabling an extended cross-correlation measurement using a 14,200 deg$^2$ sky area, which offers a more precise measurement than previous studies. We created 2D and 3D void catalogs to cross-correlate their locations with the Planck CMB lensing map and studied their average imprint signal using a stacking methodology. Applying the same procedure, we also generated a mock galaxy catalog from the WebSky simulation to serve as a basis for comparison. The 2D void analysis reveals a good agreement with the standard cosmological model, with $A_ kappa 0.08$ amplitude; that is, $S/N=13.3$, showing a higher signal-to-noise than previous studies using voids detected in the Dark Energy Survey (DES) dataset. The 3D void analysis exhibited a lower signal-to-noise ratio and demonstrated worse agreement with our mock catalog than the 2D voids. These deviations might be attributed to limitations in the mock catalog, such as imperfections in the LRG selection, as well as a potential asymmetry between the northern and southern patches of the Pan-STARRS dataset in terms of data quality. Overall, we present a significant detection of a CMB lensing signal associated with cosmic voids, largely consistent with the concordance model. Future analyses using even larger datasets also hold great promise of further sharpening these results, given their complementary nature to large-scale structure analyses.

Elucidating galaxy population properties using a model-free analysis of quadruply imaged quasar lenses from large surveys

ABSTRACT The population of strong lensing galaxies is a subset of intermediate-redshift massive galaxies, whose population-level properties are not yet well understood. In the near future, thousands of multiply imaged systems are expected to be discovered by wide-field surveys like Rubin Observatory’s Legacy Survey of Space and Time and Euclid. With the soon-to-be robust population of quadruply lensed quasars, or quads, in mind, we introduce a novel technique to elucidate the empirical distribution of the galaxy population properties. Our re-imagining of the prevailing strong lensing analysis does not fit mass models to individual lenses, but instead starts with parametric models of many galaxy populations, which include generally ignored mass distribution complexities and exclude external shear for now. We construct many mock galaxy populations with different properties and obtain populations of quads from each of them. The mock ‘observed’ population of quads is then compared to those from the mocks using a model-free analysis based on a three-dimensional subspace of directly observable quad image properties. The distance between two quad populations in the space of image properties is measured by a metric $\eta$, and the distance between their parent galaxy populations in the space of galaxy properties is measured by $\zeta$. We find a well-defined relation between $\eta$ and $\zeta$. The discovered relation between the space of image properties and the space of galaxy properties allows for the observed galaxy population properties to be estimated from the properties of their quads, which will be conducted in a future paper.

Euclid: Testing photometric selection of emission-line galaxy targets

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of which will use the NISP slitless spectrograph to record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a specific galaxy sample, but at the price of making the selection function harder to quantify. In its Wide Survey was designed to build robust statistical samples of emission-line galaxies with fluxes brighter than $ 2e-16 erg s cm $, using the Halpha -$ N ii right $ complex to measure redshifts within the range $ $. Given the expected signal-to-noise ratio of NISP spectra, at such faint fluxes a significant contamination by incorrectly measured redshifts is expected, either due to misidentification of other emission lines, or to noise fluctuations mistaken as such, with the consequence of reducing the purity of the final samples. This can be significantly ameliorated by exploiting the extensive photometric information to identify emission-line galaxies over the redshift range of interest. Beyond classical multi-band selections in colour space, machine learning techniques provide novel tools to perform this task. Here, we compare and quantify the performance of six such classification algorithms in achieving this goal. We consider the case when only the photometric and morphological measurements are used, and when these are supplemented by the extensive set of ancillary ground-based photometric data, which are part of the overall scientific strategy to perform lensing tomography. The classifiers are trained and tested on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. The best performance is obtained from either a dense neural network or a support vector classifier, with comparable results in terms of the adopted metrics. When training on on-board photometry alone, these are able to remove $87&lt;!PCT!&gt;$ of the sources that are fainter than the nominal flux limit or lie outside the $0.9&lt;z&lt;1.8$ redshift range, a figure that increases to $97&lt;!PCT!&gt;$ when ground-based photometry is included. These results show how by using the photometric information available to it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations.

Generating mock galaxy catalogues for flux-limited samples like the DESI Bright Galaxy Survey

ABSTRACT Accurate mock galaxy catalogues are crucial to validate analysis pipelines used to constrain dark energy models. We present a fast HOD-fitting method which we apply to the AbacusSummit simulations to create a set of mock catalogues for the DESI Bright Galaxy Survey, which contain r-band magnitudes and $(g-r)$ colours. The halo tabulation method fits HODs for different absolute magnitude threshold samples simultaneously, preventing unphysical HOD crossing between samples. We validate the HOD fitting procedure by fitting to real-space clustering measurements and galaxy number densities from the MXXL BGS mock, which was tuned to the SDSS and GAMA surveys. The best-fitting clustering measurements and number densities are mostly within the assumed errors, but the clustering for the faint samples is low on large scales. The best-fitting HOD parameters are robust when fitting to simulations with different realizations of the initial conditions. When varying the cosmology, trends are seen as a function of each cosmological parameter. We use the best-fitting HOD parameters to create cubic box and cut sky mocks from the AbacusSummit simulations, in a range of cosmologies. As an illustration, we compare the ${}^{0.1}M_r\lt -20$ sample of galaxies in the mock with BGS measurements from the DESI one-percent survey. We find good agreement in the number densities, and the projected correlation function is reasonable, with differences that can be improved in the future by fitting directly to BGS clustering measurements. The cubic box and cut-sky mocks in different cosmologies are made publicly available.

Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements

ABSTRACT Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the clustering signal for meaningful cosmological analysis. However, the presence of systematic contaminants can bias the observed galaxy number density, and thereby bias the galaxy two-point statistics. As the statistical uncertainties get smaller, correcting for these systematic contaminants becomes increasingly important for unbiased cosmological analysis. We present and validate a new method for understanding and mitigating both additive and multiplicative systematics in galaxy clustering measurements (two-point function) by joint inference of contaminants in the galaxy overdensity field (one-point function) using a maximum-likelihood estimator (MLE). We test this methodology with Kilo-Degree Survey-like mock galaxy catalogues and synthetic systematic template maps. We estimate the cosmological impact of such mitigation by quantifying uncertainties and possible biases in the inferred relationship between the observed and the true galaxy clustering signal. Our method robustly corrects the clustering signal to the sub-per cent level and reduces numerous additive and multiplicative systematics from $1.5 \sigma$ to less than $0.1\sigma$ for the scenarios we tested. In addition, we provide an empirical approach to identifying the functional form (additive, multiplicative, or other) by which specific systematics contaminate the galaxy number density. Even though this approach is tested and geared towards systematics contaminating the galaxy number density, the methods can be extended to systematics mitigation for other two-point correlation measurements.

SUNBIRD: a simulation-based model for full-shape density-split clustering

ABSTRACT Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-ΛCDM framework, incorporating the effects of redshift-space, Alcock–Paczynski distortions, and models of the halo–galaxy connection. Our models reach sub-per cent level accuracy down to $1 \, h^{-1}\text{Mpc}$ and are robust against different choices of galaxy–halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on ωcdm, σ8, and ns by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters.

An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations

We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies. A primary goal of this work is to produce mock galaxy catalogs for the purpose of validating existing models and methods for the mitigation of IA in weak lensing measurements. This technique can also be used to produce new, simulation-based predictions for IA and galaxy clustering. Our model is probabilistically formulated, and rests upon the assumption that the orientations of galaxies exhibit a correlation with their host dark matter (sub)halo orientation or with their position within the halo. We examine the necessary components and phenomenology of such a model by considering the alignments between (sub)halos in a cosmological dark matter only simulation. We then validate this model for a realistic galaxy population in a set of simulations in the Illustris-TNG suite. We create an HOD mock with Illustris-like correlations using our method, constraining the associated IA model parameters, with the between our model’s correlations and those of Illustris matching as closely as 1.4 and 1.1 for orientation–position and orientation–orientation correlation functions, respectively. By modeling the misalignment between galaxies and their host halo, we show that the 3-dimensional two-point position and orientation correlation functions of simulated (sub)halos and galaxies can be accurately reproduced from quasi-linear scales down to . We also find evidence for environmental influence on IA within a halo. Our publicly-available software provides a key component enabling efficient determination of Bayesian posteriors on IA model parameters using observational measurements of galaxy-orientation correlation functions in the highly nonlinear regime.

The informativeness of [C ii] line-intensity mapping as a probe of the H i content and metallicity of galaxies at the end of reionization

ABSTRACT Line-intensity mapping (LIM) experiments coming online now will survey fluctuations in aggregate emission in the [C ii] ionized carbon line from galaxies at the end of reionization. Experimental progress must be matched by theoretical reassessments of approaches to modelling and the information content of the signal. We present a new model for the halo–[C ii] connection, building upon results from the Feedback In Realistic Environments simulations suggesting that gas mass and metallicity most directly determine [C ii] luminosity. Applying our new model to an ensemble of peak-patch halo light-cones, we generate new predictions for the [C ii] LIM signal at z ≳ 6. We expect a baseline 4000-h LIM survey from the Cerro Chajnantor Atacama Telescope facility to have the fundamental sensitivity to detect the [C ii] power spectrum at a significance of 5σ at z ∼ 6, with an extended or successor Stage 2 experiment improving significance to 48σ at z ∼ 6 and achieving 11σ at z ∼ 7.5. Cross-correlation through stacking, simulated against a mock narrow-band Lyman-break galaxy survey, would yield a strong detection of the radial profile of cosmological [C ii] emission surrounding star-forming galaxies. We also analyse the role of a few of our model’s parameters through the pointwise relative entropy (PRE) of the distribution of [C ii] intensities. While the PRE signature of different model parameters can become degenerate or diminished after factoring in observational distortions, various parameters do imprint themselves differently on the one-point statistics of the intrinsic signal. Further work can pave the way to access this information and distinguish different sources of non-Gaussianity in the [C ii] LIM observation.

Modelling of surface brightness fluctuation measurements

Aims. The goal of this work is to scrutinise the surface brightness fluctuation (SBF) calculation methodology. We analysed the SBF derivation procedure, measured the accuracy of the fitted SBF under controlled conditions, retrieved the uncertainty associated with the variability of a system that is inherently stochastic, and studied the SBF reliability under a wide range of conditions. Additionally, we address the possibility of an SBF gradient detection. We also examine the problems related with biased measurements of the SBF and low luminosity sources. All of this information allows us to put forward guidelines to ensure a valid SBF retrieval. Methods. To perform all the experiments described above, we carried out Monte Carlo simulations of mock galaxies as an ideal laboratory. Knowing its underlying properties, we attempted to retrieve SBFs under different conditions. The uncertainty was evaluated through the accuracy, the precision, and the standard deviation of the fitting. Results. We demonstrate how the usual mathematical approximations taken in the SBF theoretical derivation have a negligible impact on the results and how modelling the instrumental noise reduces the uncertainty. We conducted various studies where we varied the size of the mask applied over the image, the surface and fluctuation brightness of the galaxy, its size and profile, its point spread function, and the sky background. It is worth highlighting that we find a strong correlation between having a high number of pixels within the studied mask and retrieving a low uncertainty result. We address how the standard deviation of the fitting underestimates the actual uncertainty of the measurement. Lastly, we find that, when studying SBF gradients, the result is a pixel-weighted average of all the SBFs present within the studied region. Retrieving an SBF gradient requires high-quality data and a sufficient difference in the fluctuation value through the different radii. We show how the SBF uncertainty can be obtained and we present a collection of qualitative recommendations for a safe SBF retrieval. Conclusions. Our main findings are as follows. It is important to model the instrumental noise, rather than fitting it. The target galaxies must be observed under appropriate observational conditions. In a traditional SBF derivation, one should avoid pixels with fluxes lower than ten times the SBF estimate to prevent biased results. The uncertainty associated with the intrinsic variability of the system can be obtained using sets of Monte Carlo mock galaxy simulations. We offer our computational implementation in the form of a simple code designed to estimate the uncertainty of the SBF measurement. This code can be used to predict the quality of future observations or to evaluate the reliability of those already conducted.

Joint constraints from cosmic shear, galaxy-galaxy lensing and galaxy clustering: internal tension as an indicator of intrinsic alignment modelling error

In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, galaxy-galaxy lensing and galaxy clustering data, contaminated with a range of IA scenarios. Using a simple 2-parameter IA model (NLA) in a DES Y3 like analysis, we show that the galaxy-galaxy lensing + galaxy clustering combination ( 2×2pt) is significantly more robust to IA mismodelling than cosmic shear. IA scenarios that produce up to 5σ biases for shear are seen to be unbiased at the level of ∼1σ for 2×2pt. We demonstrate that this robustness can be largely attributed to the redshift separation in galaxy-galaxy lensing, which provides a cleaner separation of lensing and IA contributions. We identify secondary factors which may also contribute, including the possibility of cancellation of higher-order IA terms in 2×2pt and differences in sensitivity to physical scales. Unfortunately this does not typically correspond to equally effective self-calibration in a 3×2pt analysis of the same data, which can show significant biases driven by the cosmic shear part of the data vector. If we increase the precision of our mock analyses to a level roughly equivalent to LSST Y1, we find a similar pattern, with considerably more bias in a cosmic shear analysis than a 2×2pt one, and significant bias in a joint analysis of the two. Our findings suggest that IA model error can manifest itself as internal tension between ξ± and γt+w data vectors. We thus propose that such tension (or the lack thereof) can be employed as a test of model sufficiency or insufficiency when choosing a fiducial IA model, alongside other data-driven methods.

Correction to: CSST large-scale structure analysis pipeline: I. Constructing reference mock galaxy redshift surveys

Correction to: CSST large-scale structure analysis pipeline: I. Constructing reference mock galaxy redshift surveys

Emission line predictions for mock galaxy catalogues: a new differentiable and empirical mapping from DESI

ABSTRACT We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H α, H β, [O ii], and [O iii]) from a galaxy’s rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman’s rank correlation coefficient ρs &amp;gt; 0.87 between predictions and observations for most lines. Using a non-linear dimensionality reduction technique, we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterize and account for biases in the spectroscopic training sets used for training and calibration of photo-z’s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.