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Abstract:

We present three catalogues of candidate active galactic nuclei (AGN) in the Euclid Quick Release (Q1) fields. For each Euclid source, we collected multi-wavelength photometric and spectroscopic information from surveys such as the Galaxy Evolution Explorer (GALEX) the Dark Energy Survey (DES), the Wide-field Infrared Survey Explorer (WISE) Spitzer the Dark Energy Spectroscopic Instrument (DESI), and the Sloan Digital Sky Survey (SDSS), including spectroscopic redshifts from public compilations when available. We investigated the AGN content of the Q1 fields using multiple selection methods. Applying Euclid colours and WISE-AllWISE cuts, we identified 292,222 and 65,131 candidates, respectively. We compiled a high-purity QSO catalogue based on DR3 information, containing 1971 candidates. Using spectroscopic information from DESI, we performed broad-line and narrow-line AGN selections, yielding 4392 AGN candidates across the Q1 fields. We investigated and refined the Euclid Q1 probabilistic random forest QSO population, selecting a refined sample of 180,666 candidates. Additionally, we performed spectral energy distribution (SED) fitting on sources with available z_ spec and utilising the derived AGN fraction, we identified 7766 AGN candidates. To improve the purity of the selection, we defined two new colour criteria (JH_I_E Y and I_E H_gz), finding 313,714 and 267,513 candidates, respectively, across the Q1 fields. We have found a total of 229,779 AGN candidates equivalent to an AGN surface density of 3641 deg^-2 for $18< 24.5$ and a subsample of 30,422 candidates corresponding to an AGN surface density of 482 deg^-2 when limiting the depth to $18< 22$. The AGN surface densities recovered are consistent with predictions based on AGN X-ray luminosity functions.

Abstract:

We show, using the pseudo-Cl technique, how to estimate cosmic shear and galaxy–galaxy lensing power spectra that are insensitive to the effects of multiple sources of lensing bias including source-lens clustering, magnification bias, and obscuration effects. All of these effects are of significant concern for ongoing and near-future Stage-IV cosmic shear surveys. Their common attribute is that they all introduce a cosmological dependence into the selection of the galaxy shear sample. Here, we show how a simple adaptation of the pseudo-Cl method can help to suppress these biases to negligible levels in a model-independent way. Our approach is based on making pixelized maps of the shear field and then using a uniform weighting of those shear maps when extracting power spectra. To produce unbiased measurements, the weighting scheme must be independent of the cosmological signal, which makes the commonly used inverse-variance weighting scheme unsuitable for cosmic shear measurements. We demonstrate this explicitly. A frequently cited motivation for using inverse-variance weights is to minimize the errors on the resultant power spectra. We find that, for a Stage-IV-like survey configuration, this motivation is not compelling: the precision of power spectra recovered from uniform-weighted maps is only very slightly degraded compared to those recovered from an inverse-variance analysis, and we predict no degradation in cosmological parameter constraints. We suggest that other 2-point statistics, such as real-space correlation functions, can be rendered equally robust to these lensing biases by applying those estimators to pixelized shear maps using a uniform weighting scheme.

Abstract:

The two-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission is aimed at performing an extensive spectroscopic survey of approximately 20–30 million H α -emitting galaxies up to a redshift of about 2. This ambitious project seeks to elucidate the nature of dark energy by mapping the three-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the three-dimensional two-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid dataset, which involves millions of galaxies. The key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including k -d tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. These methods are crucial for handling the massive volume of data efficiently. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. Those have been performed by using various mock galaxy catalogues to ensure that it meets the stringent accuracy requirement of the Euclid mission. The results indicate that the software is robust and can reliably estimate the two-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission’s timeline, highlighting the software’s capability to handle large datasets efficiently.

Abstract:

We study the constraint on f(R) gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu–Sawicki f(R) gravity model, we consider four different predictions for the ratio between the power spectrum in f(R) and that in Λ cold dark matter (ΛCDM): a fitting formula, the halo model reaction approach, ReACT, and two emulators based on dark matter only N-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering, and their cross-correlation. By running Markov chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered f(R) parameter if the data are created by a different model. For the pessimistic setting of WL, one-dimensional bias for the f(R) parameter, log<inf>10</inf>| f<inf>R</inf><inf>0</inf>|, is found to be 0.5σ when FORGE is used to create the synthetic data with log<inf>10</inf>| f<inf>R</inf><inf>0</inf>| = −5.301 and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator, BCemu. For the optimistic setting, the f(R) parameter and two main baryonic parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated for the adjustment of baryonic parameters, and the one-dimensional marginalised constraint on log<inf>10</inf>| f<inf>R</inf><inf>0</inf>| is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the ΛCDM synthetic data for WL, we obtain the prior-independent upper limit of log<inf>10</inf>| f<inf>R</inf><inf>0</inf>| < −5.6. Finally, we implement a method to include theoretical errors to avoid the bias due to inaccuracies in the nonlinear matter power spectrum prediction.