Christopher Duncan

Abstract:

We introduce a fast method to measure the conversion gain in complementary metal-oxide-semiconductor active pixel sensors, which accounts for nonlinearity and interpixel capacitance (IPC). The standard mean-variance method is biased because it assumes that pixel values depend linearly on the signal, and existing methods to correct for nonlinearity still introduce significant biases. While current IPC correction methods are prohibitively slow for a per-pixel application, our new method uses separate measurements of the IPC kernel to calculate the gain almost instantaneously. Using test data from a flight detector of the ESA Euclid mission, the IPC correction recovers the results of slower methods with 0.1% accuracy. The nonlinearity correction ensures that the estimated gain is independent of signal, correcting a bias of more than 2.5%.

Abstract:

The Euclid mission and other next-generation large-scale structure surveys will enable high-precision measurements of the cosmic matter distribution. Understanding the impact of baryonic processes such as star formation and active galactic nuclei (AGN) feedback on matter clustering is crucial to ensure precise and unbiased cosmological inference. Most theoretical models of baryonic effects to date focus on two-point statistics, neglecting higher-order contributions. This work develops a fast and accurate emulator for baryonic effects on the matter bispectrum, a key non-Gaussian statistic in the nonlinear regime. We employ high-resolution N -body simulations from the BACCO suite and apply a combination of cutting-edge techniques such as cosmology scaling and baryonification to efficiently span a large cosmological and astrophysical parameter space. A deep neural network is trained to emulate baryonic effects on the matter bispectrum measured in simulations, capturing modifications across various scales and redshifts relevant to Euclid . We validate the emulator accuracy and robustness using an analysis of Euclid mock data, employing predictions from the state-of-the-art FLAMINGO hydrodynamical simulations. The emulator reproduces baryonic suppression in the bispectrum to better than 2% for the 68% percentile across most triangle configurations for k ∈ [0.01, 20] h Mpc −1 and ensures consistency between cosmological posteriors inferred from second- and third-order weak lensing statistics. These results demonstrate that our emulator meets the high-precision requirements of the Euclid mission for at least the first data release and provides reliable forecasts of the cosmological information contained in the small-scale matter bispectrum. This underscores the potential of emulation techniques to bridge the gap between complex baryonic physics and observational data, maximising the scientific output of Euclid .

Abstract:

We present two extensive sets of 3500+1000 simulations of dark matter haloes on the past light cone and two corresponding sets of simulated (mock) galaxy catalogues that represent the spectroscopic sample of Euclid . The simulations were produced with the latest version of the code Pinocchio and provide the largest public set of simulated skies. The mock galaxy catalogues were obtained by populating haloes with galaxies using an halo occupation distribution (HOD) model extracted from the Flagship galaxy catalogue provided by Euclid Collaboration. The Geppetto set of 3500 simulated skies was obtained by tiling a 1.2 h −1 Gpc box to cover a light cone whose sky footprint is a circle with a radius of 30° for an area of 2763 deg 2 and a minimum halo mass of 1.5 × 10 11 h −1 M ⊙ . The relatively small size of the box means that this set is unsuitable for measuring very large scales. The EuclidLargeBox set consists of 1000 simulations of 3.38 h −1 Gpc and has the same mass resolution and a footprint that covers half of the sky. It excludes the Milky Way zone of avoidance. From this, we produced a set of 1000 EuclidLargeMocks on the 30° radius footprint, whose comoving volume is fully contained in the simulation box. We validated the two sets of catalogues by analysing number densities, power spectra, and two-point correlation functions to show that the Flagship spectroscopic catalogue is consistent with being one of the realisations of the simulated sets. We noted small deviations, however, that are limited to the quadrupole at k > 0.2 h Mpc −1 . We infer the cosmological parameters from these catalogues and demonstrate that using one realisation of EuclidLargeMocks in place of the Flagship mock produces the same posteriors to within the expected shift given by the sample variance. These simulated skies will be used for the galaxy clustering analysis of the Euclid Data Release 1 (DR1), and an even larger set of simulations is planned for the next releases.

Abstract:

Several new galaxy-galaxy strong gravitational lenses have been detected in the early release observations (ERO) from Euclid. The all-sky survey is expected to find 170 000 new systems, which are expected to greatly enhancing studies of dark matter and dark energy, and to constrain the cosmological parameters better. As a first step, we visually inspect all galaxies in one of the ERO fields (Perseus) to identify candidate strong-lensing systems and compared them to the predictions from convolutional neural networks (CNNs). The entire ERO dataset is too large for an expert visual inspection, however. In this paper, we therefore extend the CNN analysis to the whole ERO dataset and use different CNN architectures and methods. Using five CNN architectures, we identified 8469 strong gravitational lens candidates from IE-band cutouts of 13 Euclid ERO fields and narrowed them down to 97 through visual inspection. The sample includes 14 grade A and 31 grade B candidates. We present the spectroscopic confirmation of a strong gravitational lensing candidate, EUCL J081705.61+702348.8. The foreground lensing galaxy, an early-type system at z = 0.335, and the background source, a star-forming galaxy at z = 1.475 with [O II] emission, are both identified. The lens modelling with the Euclid strong lens modelling pipeline revealed two distinct arcs in a lensing configuration, with an Einstein radius of 1.a′3;18 ± 0.3;03. This confirms the lensing nature of the system. These findings demonstrate that CNN-based candidate selection followed by visual inspection provides an effective approach for identifying strong lenses in Euclid data. They also highlight areas for improvement in future large-scale implementations.

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.