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Phil J. Marshall

Publications and source records attributed to Phil J. Marshall.

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The LSST DESC Data Challenge 1: Generation and Analysis of Synthetic Images for Next Generation Surveys

Data Challenge 1 (DC1) is the first synthetic dataset produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST dataset. DC1 is comprised of $r$-band observations of 40 deg$^{2}$ to 10-year LSST depth. We present each stage of the simulation and analysis process: a) generation, by synthesizing sources from cosmological N-body simulations in individual sensor-visit images with different observing conditions; b) reduction using a development version of the LSST Science Pipelines; and c) matching to the input cosmological catalog for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star-galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: i) survey properties have an impact of 50\% of the statistical uncertainty for the scales and models used in DC1 ii) a selection to eliminate artifacts in the catalogs is necessary to avoid biases in the measured clustering; iii) the presence of bright objects has a significant impact (2- to 6-$σ$) in the estimated power spectra at small scales ($\ell > 1200$), highlighting the impact of blending in studies at small angular scales in LSST;

astro-ph.IM

The SAGA Survey: I. Satellite Galaxy Populations Around Eight Milky Way Analogs

We present the survey strategy and early results of the "Satellites Around Galactic Analogs" (SAGA) Survey. The SAGA Survey's goal is to measure the distribution of satellite galaxies around 100 systems analogous to the Milky Way down to the luminosity of the Leo I dwarf galaxy ($ M_r < -12.3 $). We define a Milky Way analog based on $K$-band luminosity and local environment. Here, we present satellite luminosity functions for 8 Milky Way analog galaxies between 20 to 40 Mpc. These systems have nearly complete spectroscopic coverage of candidate satellites within the projected host virial radius down to $ r_o < 20.75 $ using low redshift $gri$ color criteria. We have discovered a total of 25 new satellite galaxies: 14 new satellite galaxies meet our formal criteria around our complete host systems, plus 11 additional satellites in either incompletely surveyed hosts or below our formal magnitude limit. Combined with 13 previously known satellites, there are a total of 27 satellites around 8 complete Milky Way analog hosts. We find a wide distribution in the number of satellites per host, from 1 to 9, in the luminosity range for which there are five Milky Way satellites. Standard abundance matching extrapolated from higher luminosities predicts less scatter between hosts and a steeper luminosity function slope than observed. We find that the majority of satellites (26 of 27) are star-forming. These early results indicate that the Milky Way has a different satellite population than typical in our sample, potentially changing the physical interpretation of measurements based only on the Milky Way's satellite galaxies.

astro-ph.GA

Cosmological parameter estimation and Bayesian model comparison using VSA data

We constrain the basic comological parameters using the first observations by the Very Small Array (VSA) in its extended configuration, together with existing cosmic microwave background data and other cosmological observations. We estimate cosmological parameters for four different models of increasing complexity. In each case, careful consideration is given to implied priors and the Bayesian evidence is calculated in order to perform model selection. We find that the data are most convincingly explained by a simple flat Lambda-CDM cosmology without tensor modes. In this case, combining just the VSA and COBE data sets yields the 68 per cent confidence intervals Omega_b h^2=0.034 (+0.007, -0.007), Omega_dm h^2 = 0.18 (+0.06, -0.04), h=0.72 (+0.15,-0.13), n_s=1.07 (+0.06,-0.06) and sigma_8=1.17 (+0.25, -0.20). The most general model considered includes spatial curvature, tensor modes, massive neutrinos and a parameterised equation of state for the dark energy. In this case, by combining all recent cosmological data, we find, in particular, 95 percent limit on the tensor-to-scalar ratio R < 0.63 and on the fraction of massive neutrinos f_nu < 0.11; we also obtain the 68 per cent confidence interval w=-1.06 (+0.20, -0.25) on the equation of state of dark energy.

astro-ph