Elizabeth Johana Gonzalez

We present the first end-to-end validation of the Euclid baryon acoustic oscillation (BAO) analysis pipeline, encompassing density-field reconstruction, two-point correlation function measurement, and cosmological parameter inference. Using eight Euclid -like mock catalogues extracted from each of the four snapshots of the Flagship 1 (FS1) simulation, which is designed to replicate the statistical properties of the first Euclid data release (DR1), we assessed the performance of the two standard BAO reconstruction methods based on the Zeldovich approximation, R EC S YM and R EC I SO , in four redshift snapshots (0.9 ≤ z ≤ 1.8). The pipeline introduces several methodological advances: an emulator-based model evaluator ( Bora.jl ) combined with a Hamiltonian Monte Carlo sampler ( NUTS ), achieving a speed-up of more than 500 times relative to standard Monte Carlo Markov chains, and a semi-analytical covariance estimator ( BeXiCov+WinCov ) that enables robust error estimates with only eight mock realisations and remains stable under variations in the fiducial cosmology. Together, these components ensure computational efficiency while significantly reducing the risk of underestimating the parameter uncertainties. The two reconstruction schemes yield unbiased BAO measurements for all redshifts and user-defined choices, including the smoothing scale and fiducial cosmology. In each snapshot, reconstruction enhances the figure of merit for {Ω _m , H ₀ r _s } by a factor of ∼3, which is equivalent to tripling the effective survey volume. When we combined the four redshift bins, the improvement remained substantial, with BAO-only constraints reaching ∼10% precision on Ω _m and ∼3% on H ₀ r _s . The results from R EC S YM and R EC I SO are consistent within the uncertainties, but we recommend R EC S YM during testing because its sensitivity to covariance variations is lower. These findings establish the accuracy, robustness, and scalability of the Euclid BAO pipeline for DR1 and provide a solid foundation for future cosmological analyses.

Simulated galaxy catalogues have become an essential tool for preparing and exploiting observations from galaxy surveys. They constitute a key ingredient in modelling the systematic uncertainties present in the analysis and testing of our cosmological pipelines. However, to reach the large volume and high precision required for galaxy surveys, we generally populate dark matter haloes with galaxies following certain theoretical recipes. Such recipes contain free parameters that are calibrated by comparing simulations against observations, but the creation of galaxy mocks is a stochastic process with a large number of free parameters to calibrate. Some approaches in the literature consist of fine-tuning these parameters one at a time, or they rely on computationally expensive Markov chain Monte Carlo exploration to find a reasonable agreement with observations. Here we present a new pipeline, based on the differential evolution algorithm, that can calibrate complex galaxy mocks in a fully automated way for realistic scenarios with a large parameter space. We applied the pipeline to galaxy mocks built on a combination of halo occupation distribution and abundance-matching techniques. We show that our pipeline can properly calibrate both Λ cold dark matter (ΛCDM) and modified gravity galaxy mocks against observations, leading to the production of one of the largest f ( R ) galaxy mock catalogues. This type of calibration pipeline provides a new tool for automating the calibration of future massive galaxy mocks.

Aims . Leveraging the high-precision photometric redshifts of the Physics of the Accelerating Universe Survey (PAUS), we aim to investigate the connection between active galactic nuclei (AGNs) and galaxy pairs up to redshifts of ∼1.0. Methods . We identified 16 824 galaxy pairs with projected separations of r _p < 100 kpc and radial-velocity differences of ∆ V < 3500 km s ⁻¹ . AGNs were selected by cross-matching PAUS galaxies with multi-wavelength AGN catalogues, yielding 270 AGNs in pairs out of a parent sample of 8880 AGNs in PAUS. We constructed control samples of isolated AGNs; non-AGNs in pairs; and isolated non-AGNs matched in redshift, absolute magnitude, and environment. Results . Active galactic nuclei in pairs show progressively redder rest-frame colours with increasing redshift, particularly at redshifts of z ≳ 0.5, whereas isolated AGNs and non-AGNs in pairs show no significant colour evolution and remain at M _g − M _r ∼ 0.65. Pairs hosting AGNs are also preferentially morphologically disturbed interacting systems found at smaller projected separations and velocity offsets, consistent with more dynamically advanced interactions. Conclusions . These results provide evidence of a redshift-dependent link between galaxy interactions and AGN activity, suggesting that interactions play an increasingly important role in triggering nuclear activity at intermediate redshifts. This study highlights the unique capability of PAUS to probe AGN–galaxy interaction connections beyond the local Universe with unprecedented statistical power.

The Euclid satellite will deliver a catalogue of optically selected galaxy clusters spanning from around 2000 deg ² in Data Release (DR) 1 to around 14 000 deg ² in DR3. In this work, we assess the validity of cluster clustering (CC) models for template-fitting, which complements the full-shape methodology by providing cosmological information from the anisotropy of the redshift-space two-point correlation function (2PCF). Both methods will be used to analyse the cluster 2PCF multipoles with Euclid . We examined the multipoles of the two-point redshift-space clustering of galaxy clusters simulated with the semi-analytic PINOCCHIO code using third-order Lagrangian perturbation theory, assuming a Euclid DR1-like footprint of 500 deg ² in the northern hemisphere and 1400 deg ² in the southern hemisphere. We estimated the first three even multipoles of the 2PCF and associated covariance matrix from 1000 DR1-like synthetic catalogues. We studied the impact of modelling the relevant non-linearities, halo bias, and photometric redshift uncertainties on the 2PCF. We applied three clustering models to the mock catalogues at 0 < z < 2 and with a virial mass of M _vir > 10 ¹⁴ h ⁻¹ M _⊙ under realistic and optimistic photometric redshift uncertainty scenarios. We formulated a set of permissive and conservative criteria that ought to be fulfilled by the multipole cut-off scales and validated them against 100 mock catalogues via an inference of the growth rate multiplied by the matter power spectrum normalisation parameter, fσ ₈ . We tested the dispersion, Scoccimarro, and Taruya–Nishimichi–Saito models. We find that the simplest of the three (i.e. the dispersion model) yields unbiased inferences on fσ ₈ from CC down to 10 h ⁻¹ Mpc in a DR1-like setting. All clustering models provide very similar goodness-of-fit metrics in the presence of DR1-like cluster redshift uncertainties.

A critical step in creating realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables, while taking into account the redshift-dependent galaxy formation and evolution processes. In this work, we developed an efficient framework named Halo Optimised Lightcone Constructor ( HOLCon ) for the post facto construction of dark matter halo light cones from simulations. We used these light cones to generate a mock galaxy catalogue. The framework includes a module to optimise the light cone’s orientation within the simulation box, minimising repeated structures when the survey volume exceeds a single box, which is a common challenge in modern surveys. A linear interpolation scheme tracks the evolution of halo properties across snapshots. Applied to the publicly available Uchuu simulation, we constructed a light cone of 50 deg ² and extending up to z = 10, providing representative coverage of deep fields of Stage IV surveys. We validated the light cone for cosmological applications by comparing the dark matter halo clustering in the light cone with those from the original simulation snapshots. Subsequently, we made the galaxy-halo connection on the light cone with a redshift-extended version of the SciPIC algorithm, producing a comprehensive set of descriptive galaxy attributes. HOLCon leverages Dask , a scalable parallel computing Pythonic framework for the fast construction of dark matter halo light cones, enabling the rapid creation of multiple statistical realisations essential for robust cosmological inference. The generated galaxy mock allows us to make predictions for the clustering of H α emitters, making it a useful resource in cosmology studies.

The intrinsic alignments of galaxies are a potentially major contaminant among cosmological analyses of weak gravitational lensing. In this work, we constructed a semi-analytic model of galaxy ellipticities and alignments in the Euclid Flagship simulation to predict this contamination in Euclid ’s weak lensing observations. Galaxy shapes and orientations were determined by the corresponding properties of the host haloes in the underlying N -body simulation, as well as the relative positions of galaxies within their halo. Alignment strengths were moderated via stochastic misalignments, separately for central and satellite galaxies, and were dependent on the galaxy’s redshift, luminosity, and rest-frame colour. The resulting model was calibrated against galaxy ellipticity statistics from the COSMOS Survey, selected alignment measurements based on Sloan Digital Sky Survey samples, and galaxy orientations extracted from the Horizon-AGN hydrodynamic simulation at redshift z = 1. The best-fit model has a total of 12 alignment parameters and generally reproduces the calibration data sets well within the 1 σ statistical uncertainties of the observations and the Flagship simulation, with notable exceptions for the most luminous subsamples on small physical scales. The statistical power of the calibration data and the volume of the single Flagship realisation are still too small to provide informative prior ranges for intrinsic alignment amplitudes in relevant galaxy samples. Given these uncertainties, we predict that Euclid end-of-mission tomographic weak gravitational lensing two-point statistics will end up modified due to intrinsic alignments, by up to 10%.

We present , a simulation-based forward model algorithm calibrated to reproduce optical cluster observables in line with measurements taken with the Euclid space telescope. The main deliverable of is a correction for stacked surface-density profiles, binned in terms of richness and redshift, that accounts for selection-related systematic effects. These corrections take into account the modification to the stacked weak lensing signal from richness-selected samples of clusters identified in the photometric Euclid survey compared with an unbiased reference sample. In this work, we focus on the Euclid richness definition currently foreseen for the cosmological analysis, which does not apply a colour selection. Euclid also provides an alternative richness estimate based on a red-sequence galaxy selection, which is not considered here. The algorithm processes N-body simulations by painting galaxies with a halo-occupation model and emulating the survey’s detection and richness-assignment algorithms. We implemented a novel algorithm to estimate the optical cluster centres from galaxy-projected densities and validated it against the official Euclid pipelines. The effect of baryonic physics on the halo density profiles is incorporated through a correction calibrated on hydrodynamical simulations, yielding total-matter profiles consistent with those measured in the reference hydrodynamical simulations. In validation against hydrodynamical simulations, the baryon-corrected excess surface density is in agreement to within ( 2,%) for cluster-centric radii, CosmoPostProcess CosmoPostProcess 0.1,,5 ,h^ -1 , ). To assess the impact of the different contributions to the selection bias, we performed dedicated tests that included variations of both cosmological parameters and the parameters of the mass-richness relation. Across all these tests, the selection bias induced by projection alone follows a robust pattern: a large-scale structure that is physically correlated with the main halo and projected along the line of sight enhances the stacked surface density profile near the transition from one-halo to two-halo dominance, with a peak radius of about (1,h^ , ) and an amplitude of about (20!-!40,%), with a dependence on richness and redshift. This behaviour is mild at low and intermediate redshifts (złesssim0.7), where the impact remains at the level of a few per cent, but it becomes increasingly relevant at higher redshift (z , ). As an outcome of this analysis, the framework delivers radial profile corrections with associated uncertainties, combining the selection bias from projection effects with the impact of baryonic physics and miscentring. These corrections will be a key ingredient to ensure controlled systematics in the Euclid DR1 galaxy cluster cosmological analysis. Mpc -1 Mpc with a more pronounced enhancement of the transition-scale peak. Finally, baryonic modifications remain sub-dominant outside the core, with an impact of about ( 2,%) beyond (r≳ 0.3,h^ -1 Mpc CosmoPostProcess

The Euclid galaxy survey will provide unprecedented constraints on cosmology, but achieving unbiased results will require an optimal characterisation and mitigation of systematic effects. The of galaxies are one of the dominant contaminants of the and probes. In this work, we assess modelling choices for Euclid DR1 3 models, and , along with several variations. Our analyses combine three perspectives: (i) the constraining power on the and cosmological parameters for each model, (ii) the bias that results when the analysis model differs from the model used to generate the synthetic data vector, and (iii) the degeneracies between and nuisance parameters. Amongst the models analysed, the redshift-dependent TATT model ( , with a constraining power similar to simpler models, making it a suitable choice for Euclid DR1 3 ia wl ggl ia 2,pt analyses by using synthetic data vectors and comparing the performance of the two most commonly used ia nla tatt ia ia ia ia photo-z ia provides the most flexible description of ia ia 2,pt analyses.

High-fidelity galaxy mocks are crucial for validating analysis pipelines and for cosmological inference. In this context, the Science Pipeline at PIC ( ) is a pipeline specifically designed for the fast generation of synthetic galaxy catalogues from the halo properties identified in cosmological simulations. delivers galaxy catalogues that aim to reproduce the observed luminosity function and clustering above a given flux detection limit over a wide redshift range. In this work, we introduce , an automated pipeline that calibrates the parameters that set the main mock galaxy properties, namely number density, luminosities, colours, and positions. The pipeline was applied to the Euclid Flagship 2 Wide and Deep halo catalogues, specifically built to support the wide and deep surveys. Compared to the recently released Flagship 2 Wide mock, our calibrated version improves the clustering predictions by approximately 50% based on chi-squared values. Furthermore, we produced the Euclid Deep mock catalogue, which reaches up to z = 10 by populating a light cone and a complementary snapshot at z = 0. We validated these catalogues using measurements from spectroscopic and photometric galaxy surveys, as well as with results from a hydrodynamical simulation. The obtained good agreement (within 15% for most of the samples) in the clustering predictions across the different galaxy samples considered, validates our calibration strategy and demonstrates the strong predictive power of the generated mocks. This pipeline will allow us to improve the methodology applied for assigning the galaxy properties and will ensure that the galaxy mocks remain up-to-date by incorporating constraints from upcoming observational data into the calibration procedure. SciPIC SciPIC SciPICal Euclid

The extraction of cosmological information from two-point statistics critically relies on the assumed form of their likelihood. Although a Gaussian likelihood is generally adopted, the first paper of this series showed that the distribution of power-spectrum estimates exhibits non-Gaussian features on both large and small scales (k<0.5, with their amplitudes depending on the survey volume, masking, and shot noise. In this work, we account for this skewness in parameter inference by modelling the likelihood through an Edgeworth expansion. This procedure involves the complete skewness tensor, composed of one-point, two-point, and three-point correlators. To simplify the calculations of this expansion, we performed a change in the basis, which reduced the precision matrix to the identity. In this basis, the off-diagonal elements of the skewness tensor are consistent with zero, while the amplitude of its diagonal match the level expected for a Gaussian underlying field. We performed a parameter inference with this likelihood model and found that including only the diagonal part of the skewness is sufficient; whereas incorporating the full skewness tensor injects noise without improving accuracy. Despite the estimated excess skewness in the original basis, the cosmological constraints remain effectively unchanged when adopting a Gaussian likelihood or considering the more complete Edgeworth expansion, with variations in the figure of merit of cosmological parameters between the two cases below 5%. This result remains unchanged against variations of the survey volume and geometry, scale-cut, as well as the two-point statistics (i.e. power spectrum or correlation function). Using 10, 000 cloned Euclid large mocks based on realistic galaxy catalogues with characteristics approximating future Euclid data, we found no detectable excess skewness on intermediate scales, due to the level of shot noise expected for the Euclid spectroscopic sample. We conclude that the Gaussian likelihood assumption is robust for Euclid two-point statistics analyses in both Fourier and the configuration space.