We analyze the evolution of massive (log 10[M ⋆/M ⊙] > 10) galaxies at z ∼ 4–8 selected from JWST Cosmic Evolution Early Release Survey (CEERS). We infer the physical properties of all galaxies in the CEERS NIRCam imaging through spectral energy distribution (SED) fitting with dense basis to select a sample of high-redshift massive galaxies. Where

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of Euclid, 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

Verifying the fully kinematic nature of the long-known cosmic microwave background (CMB) dipole is of fundamental importance in cosmology. In the standard cosmological model with the Friedman–Lemaitre–Robertson–Walker (FLRW) metric from the inflationary expansion, the CMB dipole should be entirely kinematic. Any non-kinematic CMB dipole component

In anticipation of the upcoming Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model with advanced photoionisation models to construct emission-line catalogues. This has allowed us to self-consistently model

Context. The Euclid space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the ΛCDM model. Aims. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies