Hintze Centre for Astrophysical Surveys

Abstract:

ABSTRACT Intensity mapping (IM) with neutral hydrogen is a promising avenue to probe the large-scale structure of the Universe. In this paper, we demonstrate that using the 64-dish MeerKAT radio telescope as a connected interferometer, it is possible to make a statistical detection of H i in the post-reionization Universe. With the MIGHTEE (MeerKAT International GHz Tiered Extragalactic Exploration) survey project observing in the L-band (856 MHz < ν < 1712 MHz, z < 0.66), we can achieve the required sensitivity to measure the H i IM power spectrum on quasi-linear scales, which will provide an important complementarity to the single-dish IM MeerKAT observations. We present a purpose-built simulation pipeline that emulates the MIGHTEE observations and forecasts the constraints that can be achieved on the H i power spectrum at z = 0.27 for k > 0.3 $\rm {Mpc}^{-1}$ using the foreground avoidance method. We present the power spectrum estimates with the current simulation on the COSMOS field that includes contributions from H i, noise, and point-source models constructed from the observed MIGHTEE data. The results from our visibility-based pipeline are in qualitative agreement to the already available MIGHTEE data. This paper demonstrates that MeerKAT can achieve very high sensitivity to detect H i with the full MIGHTEE survey on quasi-linear scales (signal-to-noise ratio >7 at k = 0.49 $\rm {Mpc}^{-1}$) that are instrumental in probing cosmological quantities such as the spectral index of fluctuation, constraints on warm dark matter, the quasi-linear redshift space distortions, and the measurement of the H i content of the Universe up to z ∼ 0.5.

Abstract:

ABSTRACT Using high-resolution Hubble Space Telescope imaging data, we perform a visual morphological classification of ∼36 000 galaxies at z < 1 in the deep extragalactic visible legacy survey/cosmological evolution survey region. As the main goal of this study, we derive the stellar mass function (SMF) and stellar mass density (SMD) sub-divided by morphological types. We find that visual morphological classification using optical imaging is increasingly difficult at z > 1 as the fraction of irregular galaxies and merger systems (when observed at rest-frame UV/blue wavelengths) dramatically increases. We determine that roughly two-thirds of the total stellar mass of the Universe today was in place by z ∼ 1. Double-component galaxies dominate the SMD at all epochs and increase in their contribution to the stellar mass budget to the present day. Elliptical galaxies are the second most dominant morphological type and increase their SMD by ∼2.5 times, while by contrast, the pure-disc population significantly decreases by $\sim 85{{\ \rm per\ cent}}$. According to the evolution of both high- and low-mass ends of the SMF, we find that mergers and in situ evolution in discs are both present at z < 1, and conclude that double-component galaxies are predominantly being built by the in situ evolution in discs (apparent as the growth of the low-mass end with time), while mergers are likely responsible for the growth of ellipticals (apparent as the increase of intermediate/high-mass end).

Abstract:

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

Abstract:

ABSTRACT We study the Fundamental Plane (FP) for a volume- and luminosity-limited sample of 560 early-type galaxies from the SAMI survey. Using r-band sizes and luminosities from new multi-Gaussian expansion photometric measurements, and treating luminosity as the dependent variable, the FP has coefficients a = 1.294 ± 0.039, b = 0.912 ± 0.025, and zero-point c = 7.067 ± 0.078. We leverage the high signal-to-noise ratio of SAMI integral field spectroscopy, to determine how structural and stellar population observables affect the scatter about the FP. The FP residuals correlate most strongly (8σ significance) with luminosity-weighted simple stellar population (SSP) age. In contrast, the structural observables surface mass density, rotation-to-dispersion ratio, Sérsic index, and projected shape all show little or no significant correlation. We connect the FP residuals to the empirical relation between age (or stellar mass-to-light ratio Υ⋆ ) and surface mass density, the best predictor of SSP age amongst parameters based on FP observables. We show that the FP residuals (anti)correlate with the residuals of the relation between surface density and Υ⋆ . This correlation implies that part of the FP scatter is due to the broad age and Υ⋆ distribution at any given surface mass density. Using virial mass and Υ⋆, we construct a simulated FP and compare it to the observed FP. We find that, while the empirical relations between observed stellar population relations and FP observables are responsible for most (75 per cent) of the FP scatter, on their own they do not explain the observed tilt of the FP away from the virial plane.