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We present stellar-dynamical measurements of the central supermassive black hole (SMBH) in the S0 galaxy NGC 307, using adaptive-optics IFU data from VLT-SINFONI. We investigate the effects of including dark-matter haloes as well as multiple stellar components with different mass-to-light (M/L) ratios in the dynamical modelling. Models with no halo and a single stellar component yield a relatively poor fit with a low value for the SMBH mass [(7.0±1.0)×107M⊙] and a high stellarM/L ratio (ϒK =1.3±0.1). Adding a halo produces a much better fit, with a significantly larger SMBH mass [(2.0 ± 0.5) × 108M⊙] and a lower M/L ratio (ϒK = 1.1 ± 0.1). A model with no halo but with separate bulge and disc components produces a similarly good fit, with a slightly larger SMBH mass [(3.0 ± 0.5) × 108M⊙] and an identical M/L ratio for the bulge component, though the disc M/L ratio is biased high (ϒK, disc = 1.9 ± 0.1). Adding a halo to the two-stellar-component model results in a much more plausible disc M/L ratio of 1.0 ± 0.1, but has only a modest effect on the SMBH mass [(2.2 ± 0.6) × 108M⊙] and leaves the bulge M/L ratio unchanged. This suggests that measuring SMBH masses in disc galaxies using just a single stellar component and no halo has the same drawbacks as it does for elliptical galaxies, but also that reasonably accurate SMBH masses and bulge M/L ratios can be recovered (without the added computational expense of modelling haloes) by using separate bulge and disc components. [ABSTRACT FROM AUTHOR]

Photometric redshift uncertainties are a major source of systematic error for ongoing and future photometric surveys. We study different sources of redshift error caused by choosing a suboptimal redshift histogram bin width and propose methods to resolve them. The selection of a too large bin width is shown to oversmooth small-scale structure of the radial distribution of galaxies. This systematic error can significantly shift cosmological parameter constraints by up to 6σ for the dark energy equation-of-state parameter w. Careful selection of bin width can reduce this systematic by a factor of up to 6 as compared with commonly used current binning approaches. We further discuss a generalized resampling method that can correct systematic and statistical errors in cosmological parameter constraints caused by uncertainties in the redshift distribution. This can be achieved without any prior assumptions about the shape of the distribution or the form of the redshift error. Our methodology allows photometric surveys to obtain unbiased cosmological parameter constraints using a minimum number of spectroscopic calibration data. For a DES-like galaxy clustering forecast, we obtain unbiased results with respect to errors caused by suboptimal histogram bin width selection, using only 5k representative spectroscopic calibration objects per tomographic redshift bin. [ABSTRACT FROM AUTHOR]

We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning routine ADABOOST (hereafter RDF). We select a list of 85 easily measured (or derived) photometric quantities (or 'features') and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNNs) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18 percent and decreases the catastrophic outlier rate by 32 percent. We further compare the redshift estimate using RDF with those from two different aNNs, and with photometric redshifts available from the Sloan Digital Sky Survey (SDSS). We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a machine learning redshift while reducing both the catastrophic outlier rate by up to 43 percent, and the redshift error by up to 25 percent. When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+z) by 36?per?cent from 0.066 to 0.041, and decreases the fraction of catastrophic outliers by 57?per?cent from 2.32 to 0.99?per?cent. [ABSTRACT FROM AUTHOR]