Professor Pedro Ferreira

Abstract:

The near-infrared calibration unit (NI-CU) on board Euclid’s Near-Infrared Spectrometer and Photometer (NISP) is the first astronomical calibration lamp based on light-emitting diodes (LEDs) to be operated in space. Euclid is a mission in ESA’s Cosmic Vision 2015–2025 framework to explore the dark universe and provide a next-level characterisation of the nature of gravitation, dark matter, and dark energy. Calibrating photometric and spectrometric measurements of galaxies to better than 1.5% accuracy in a survey homogeneously mapping ∼14 000 deg2 of extragalactic sky requires a very detailed characterisation of near-infrared (NIR) detector properties as well as constant monitoring of them in flight. To cover two of the main contributions – relative pixel-to-pixel sensitivity and non-linearity characteristics – and to support other calibration activities, NI-CU was designed to provide spatially approximately homogeneous (<12% variations) and temporally stable illumination (0.1–0.2% over 1200 s) over the NISP detector plane with minimal power consumption and energy dissipation. NI-CU covers the spectral range ∼[900,1900] nm – at cryo-operating temperature – at five fixed independent wavelengths to capture wavelength-dependent behaviour of the detectors, with fluence over a dynamic range of ≳100 from ∼15 ph s−1 pixel−1 to >1500 ph s−1 pixel−1. For this functionality, NI-CU is based on LEDs. We describe the rationale behind the decision and design process, the challenges in sourcing the right LEDs, and the qualification process and lessons learned. We also provide a description of the completed NI-CU, its capabilities, and performance as well as its limits. NI-CU has been integrated into NISP and the Euclid satellite, and since Euclid’s launch in July 2023, it has started supporting survey operations.

Abstract:

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double Sérsic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterisation. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures and irregular shapes have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from Hubble Space Telescope (HST) data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function (PSF) of HST galaxy images. These can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly as well as conditionally. In the latter case, we fine-tune the interpolation between latent space vectors of sample galaxies to directly obtain new realistic objects following a specific Sérsic index and half-light radius distribution. Furthermore, we show that the distribution of galaxy structural and morphological parameters of our generative model matches the distribution of the input HST training data, proving the capability of the model to produce realistic shapes. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-Sérsic counterparts, thereby creating two separate branches that only differ in the complexity of their shapes. Using the Kaiser, Squires, and Broadhurst shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of (6.9 ± 0.6)×10-3 for a realistic magnitude-Sérsic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of (4.0 ± 0.9)×10-3 independent of the shape measurement method. This makes complex morphology relevant for stage IV weak lensing surveys, exceeding the full error budget of the Euclid Wide Survey (Δμ1,2 < 2 × 103).

Abstract:

Context. Cluster cosmology can benefit from combining multi-wavelength studies. In turn, these studies benefit from a characterisation of the correlation coefficients among different mass-observable relations. Aims. In this work, we aim to provide information on the scatter, skewness, and covariance of various mass-observable relations in galaxy clusters in cosmological hydrodynamic simulations. This information will help future analyses improve the general approach to accretion histories and projection effects, as well as to model mass-observable relations for cosmology studies. Methods. We identified galaxy clusters in Magneticum Box2b simulations with masses of M200c > 1014 M⊙ at redshifts of z = 0.24 and z = 0.90. Our analysis included Euclid-derived properties such as richness, stellar mass, lensing mass, and concentration. Additionally, we investigated complementary multi-wavelength data, including X-ray luminosity, integrated Compton-y parameter, gas mass, and temperature. We then examined the impact of projection effects on mass-observable residuals and correlations. Results. We find that at intermediate redshift (z = 0.24), projection effects have the greatest impact of lensing concentration, richness, and gas mass in terms of the scatter and skewness of the log-residuals of scaling relations. The contribution of projection effects can be significant enough to boost a spurious hot-versus cold-baryon correlations and consequently hide underlying correlations due to halo accretion histories. At high redshift (z = 0.9), the richness has a much lower scatter (of log-residuals), while the quantity that is most impacted by projection effects is the lensing mass. The lensing concentration reconstruction, in particular, is affected by deviations of the reduced-shear profile shape from that derived using a Navarro-Frenk-White (NFW) profile; the amount of interlopers in the line of sight, on the other hand, is not as important.

Abstract:

The Euclid mission is generating a vast amount of imaging data in four broadband filters at a high angular resolution. This data will allow for the detailed study of mass, metallicity, and stellar populations across galaxies that will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. Here, we investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity, and age. We generated noise-free synthetic high-resolution (100 pc × 100 pc) imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images were generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We used a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a ≤0.130 dex scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but they still contain significant information that originates from underlying correlations at a sub-kiloparsec scales between stellar mass surface density and stellar population properties. As a corollary, we show that TNG50 follows a spatially resolved mass-metallicity relation that is consistent with observations. Due to its relatively low computational and time requirements, which has a time-frame of minutes without dedicated high performance computing infrastructure once it has been trained, our method allows for fast and robust estimates of the stellar mass surface density distributions of nearby galaxies from four-filter Euclid imaging data. Equivalent estimates of stellar population properties (stellar metallicity and age) are less robust but still hold value as first-order approximations across large samples.

Abstract:

The ability to measure unbiased weak-lensing (WL) masses is a key ingredient to exploit galaxy clusters as a competitive cosmological probe with the ESA Euclid survey or future missions. We investigate the level of accuracy and precision of cluster masses measured with the Euclid data processing pipeline. We use the DEMNUni-Cov N-body simulations to assess how well the WL mass probes the true halo mass, and, then, how well WL masses can be recovered in the presence of measurement uncertainties. We consider different halo mass density models, priors, and mass point estimates, that is the biweight, mean, and median of the marginalised posterior distribution and the maximum likelihood parameter. WL mass differs from true mass due to, for example, the intrinsic ellipticity of sources, correlated or uncorrelated matter and large-scale structure, halo triaxiality and orientation, and merging or irregular morphology. In an ideal scenario without observational or measurement errors, the maximum likelihood estimator is the most accurate, with WL masses biased low by {bM} =a-14.6-±-1.7% on average over the full range M200c > 5×1013 M⊙ and z < 1. Due to the stabilising effect of the prior, the biweight, mean, and median estimates are more precise, that is with smaller intrinsic scatter. The scatter decreases with increasing mass and informative priors can significantly reduce the scatter. Halo mass density profiles with a truncation provide better fits to the lensing signal, while the accuracy and precision are not significantly affected. We further investigate the impact of various additional sources of systematic uncertainty on the WL mass estimates, namely the impact of photometric redshift uncertainties and source selection, the expected performance of Euclid cluster detection algorithms, and the presence of masks. Taken in isolation, we find that the largest effect is induced by non-conservative source selection with {bM} =a-33.4-±-1.6%. This effect can be mostly removed with a robust selection. As a final Euclid-like test, we combine systematic effects in a realistic observational setting and find {bM} =a-15.5-±-2.4% under a robust selection. This is very similar to the ideal case, though with a slightly larger scatter mostly due to cluster redshift uncertainty and miscentering.