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Abstract:

Abstract In this work we study a robust, Ks-band complete, spectroscopically-confirmed sample of 104 unobscured (Type-1) quasars within the COSMOS and XMM-LSS fields of the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Survey, at 0.60 < zspec < 3.41. The quasars are selected via gJKs colour-space and, with 1.3-GHz flux-densities reaching rms ≈ 3.0 μ Jy beam−1, we find a radio-loudness fraction of 5percnt. Thanks to the deep, multiwavelength datasets that are available over these fields, the properties of radio-loud and radio-quiet quasars can be studied in a statistically-robust way, with the emphasis of this work being on the active-galactic-nuclei (AGN)-related and star-formation-related contributions to the total radio emission. We employ multiple star-formation-rate estimates for the analysis so that our results can be compared more-easily with others in the literature, and find that the fraction of sources that have their radio emission dominated by the AGN crucially depends on the SFR estimate that is derived from the radio luminosity. When redshift dependence is not taken into account, a larger fraction of sources is classed as having their radio emission dominated by the AGN. When redshift dependence is considered, a larger fraction of our sample is tentatively classed as ‘starbursts’. We also find that the fraction of (possible) starbursts increases with redshift, and provide multiple suggestions for this trend.

Abstract:

Strong gravitational-lensing systems with multiple source planes are powerful tools for probing the density profiles and dark matter substructure of galaxies. The ratio of the Einstein radii is related to the dark energy equation of state through the cosmological scaling factor β . Galaxy-scale double-source-plane lenses (DSPLs) are extremely rare, however. We report the discovery of four new galaxy-scale DSPL candidates in the Euclid Quick Release 1 (Q1) data. These systems were initially identified through a combination of machine-learning lens-finding models and subsequent visual inspection from citizens and experts. We applied the widel -used LensPop lens-forecasting model to predict that the full Euclid survey will discover 1700 DSPLs. This scales to 6 ± 3 DSPLs in 63 deg 2 , which is the area of Q1. The number of discoveries in this work is broadly consistent with this forecast. We present lens models for each DSPL and infer their β values. Our initial Q1 sample demonstrates that Euclid promises to discover these rare objects.

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, log10|fR0|, is found to be 0.5σ when FORGE is used to create the synthetic data with log10|fR0| = −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 log10|fR0| 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 log10|fR0| < −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.