Hintze Centre for Astrophysical Surveys

Abstract:

We present an overview and description of the e-MERGE Survey (e-MERLIN Galaxy Evolution Survey) Data Release 1 (DR1), a large program of high-resolution 1.5-GHz radio observations of the GOODS-N field comprising ∼140 h of observations with enhanced-Multi-Element Remotely Linked Interferometer Network (e-MERLIN) and ∼40 h with the Very Large Array (VLA). We combine the long baselines of e-MERLIN (providing high angular resolution) with the relatively closely packed antennas of the VLA (providing excellent surface brightness sensitivity) to produce a deep 1.5-GHz radio survey with the sensitivity (⁠∼1.5μ Jy beam−1), angular resolution (0.2–0.7 arcsec) and field-of-view (∼15 × 15 arcmin2) to detect and spatially resolve star-forming galaxies and active galactic nucleus (AGN) at z ≳ 1. The goal of e-MERGE is to provide new constraints on the deep, sub-arcsecond radio sky which will be surveyed by SKA1-mid. In this initial publication, we discuss our data analysis techniques, including steps taken to model in-beam source variability over an ∼20-yr baseline and the development of new point spread function/primary beam models to seamlessly merge e-MERLIN and VLA data in the uv plane. We present early science results, including measurements of the luminosities and/or linear sizes of ∼500 galaxies selected at 1.5 GHz. In combination with deep Hubble Space Telescope observations, we measure a mean radio-to-optical size ratio of re-MERGE/rHST ∼ 1.02 ± 0.03, suggesting that in most high-redshift galaxies, the ∼GHz continuum emission traces the stellar light seen in optical imaging. This is the first in a series of papers that will explore the ∼kpc-scale radio properties of star-forming galaxies and AGN in the GOODS-N field observed by e-MERGE DR1.

Abstract:

We study a sample of 148 early-type galaxies in the Coma cluster using SDSS photometry and spectra, and calibrate our results using detailed dynamical models for a subset of these galaxies, to create a precise benchmark for dynamical scaling relations in high-density environments. For these galaxies, we successfully measured global galaxy properties, modelled stellar populations, and created dynamical models, and support the results using detailed dynamical models of 16 galaxies, including the two most massive cluster galaxies, using data taken with the SAURON IFU. By design, the study provides minimal scatter in derived scaling relations due to the small uncertainty in the relative distances of galaxies compared to the cluster distance. Our results demonstrate low (≤55 per cent for 90th percentile) dark matter fractions in the inner 1Re of galaxies. Owing to the study design, we produce the tightest, to our knowledge, IMF–σe relation of galaxies, with a slope consistent with that seen in local galaxies. Leveraging our dynamical models, we transform the classical Fundamental Plane of the galaxies to the Mass Plane. We find that the coefficients of the Mass Plane are close to predictions from the virial theorem, and have significantly lower scatter compared to the Fundamental Plane. We show that Coma galaxies occupy similar locations in the (M*–Re) and (M*−σe) relations as local field galaxies but are older. This, and the fact we find only three slow rotators in the cluster, is consistent with the scenario of hierarchical galaxy formation and expectations of the kinematic morphology–density relation.

Abstract:

We propose a flexible method for estimating luminosity functions (LFs) based on kernel density estimation (KDE), the most popular nonparametric density estimation approach developed in modern statistics, to overcome issues surrounding the binning of LFs. One challenge in applying KDE to LFs is how to treat the boundary bias problem, as astronomical surveys usually obtain truncated samples predominantly due to the flux-density limits of surveys. We use two solutions, the transformation KDE method ( ) and the transformation–reflection KDE method ( ) to reduce the boundary bias. We develop a new likelihood cross-validation criterion for selecting optimal bandwidths, based on which the posterior probability distribution of the bandwidth and transformation parameters for and are derived within a Markov Chain Monte Carlo sampling procedure. The simulation result shows that and perform better than the traditional binning method, especially in the sparse data regime around the flux limit of a survey or at the bright end of the LF. To further improve the performance of our KDE methods, we develop the transformation–reflection adaptive KDE approach ( ). Monte Carlo simulations suggest that it has good stability and reliability in performance, and is around an order of magnitude more accurate than using the binning method. By applying our adaptive KDE method to a quasar sample, we find that it achieves estimates comparable to the rigorous determination in a previous work, while making far fewer assumptions about the LF. The KDE method we develop has the advantages of both parametric and nonparametric methods.