Shedding light on the galaxy luminosity function

Johnston, Russell

doi:10.1007/s00159-011-0041-9

Cited by 69 publications

(41 citation statements)

References 385 publications

(533 reference statements)

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“…To sample the population of the former, we rely on luminosity functions. They are observationally well determined quantities which measure the comoving space density of galaxies, see [26] for a review and [27] for a compilation of recent results. We discriminate between blue and red galaxies and use separate luminosity functions to model these two populations.…”

Section: Image Simulationsmentioning

confidence: 99%

The redshift distribution of cosmological samples: a forward modeling approach

Herbel

Kacprzak

Amara

et al. 2017

J. Cosmol. Astropart. Phys.

137

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Determining the redshift distribution n(z) of galaxy samples is essential for several cosmological probes including weak lensing. For imaging surveys, this is usually done using photometric redshifts estimated on an object-by-object basis. We present a new approach for directly measuring the global n(z) of cosmological galaxy samples, including uncertainties, using forward modeling. Our method relies on image simulations produced using UFig (Ultra Fast Image Generator) and on ABC (Approximate Bayesian Computation) within the MCCL (Monte-Carlo Control Loops) framework. The galaxy population is modeled using parametric forms for the luminosity functions, spectral energy distributions, sizes and radial profiles of both blue and red galaxies. We apply exactly the same analysis to the real data and to the simulated images, which also include instrumental and observational effects. By adjusting the parameters of the simulations, we derive a set of acceptable models that are statistically consistent with the data. We then apply the same cuts to the simulations that were used to construct the target galaxy sample in the real data. The redshifts of the galaxies in the resulting simulated samples yield a set of n(z) distributions for the acceptable models. We demonstrate the method by determining n(z) for a cosmic shear like galaxy sample from the 4band Subaru Suprime-Cam data in the COSMOS field. We also complement this imaging data with a spectroscopic calibration sample from the VVDS survey. We compare our resulting posterior n(z) distributions to the one derived from photometric redshifts estimated using 36 photometric bands in COSMOS and find good agreement. This offers good prospects for applying our approach to current and future large imaging surveys.

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Section: Image Simulationsmentioning

confidence: 99%

The redshift distribution of cosmological samples: a forward modeling approach

Herbel

Kacprzak

Amara

et al. 2017

J. Cosmol. Astropart. Phys.

137

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“…The galaxies are drawn from galaxy luminosity functions φ (see e.g. [49,50]). We refer to a galaxy luminosity function as the number of galaxies N for comoving volume V and absolute magnitude M :…”

Section: Galaxy Luminosity Functionsmentioning

confidence: 99%

Forward modeling of spectroscopic galaxy surveys: application to SDSS

Fagioli

Riebartsch

Nicola

et al. 2018

J. Cosmol. Astropart. Phys.

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Galaxy spectra are essential to probe the spatial distribution of galaxies in our Universe. To better interpret current and future spectroscopic galaxy redshift surveys, it is important to be able to simulate these data sets. We describe Uspec, a forward modeling tool to generate galaxy spectra taking into account some intrinsic galaxy properties as well as instrumental responses of a given telescope. The model for the intrinsic properties of the galaxy population, i.e., the luminosity functions, and size and spectral coefficients distributions, was developed in an earlier work for broad-band imaging surveys [1], and we now aim to test the model further using spectroscopic data. We apply Uspec to the SDSS/CMASS sample of Luminous Red Galaxies (LRGs). We construct selection cuts that match those used to build this LRG sample, which we then apply to data and simulations in the same way. The resulting real and simulated average spectra show a good statistical agreement overall, with residual differences likely coming from a bluer galaxy population of the simulated sample. We also do not explore the impact of non-solar element ratios in our simulations. For a quantitative comparison, we perform Principal Component Analysis (PCA) of the sets of spectra. By comparing the PCs constructed from simulations and data, we find good agreement for all components. The distributions of the eigencoefficients also show an appreciable overlap. We are therefore able to properly simulate the LRG sample taking into account the SDSS/BOSS instrumental responses. The differences between the two samples can be ascribed to the intrinsic properties of the simulated galaxy population, which can be reduced by further improvements of our modelling method in the future. We discuss how these results can be useful for the forward modeling of upcoming large spectroscopic surveys.

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“…The lu-minosity function tells us about the relative abundance of different luminosity objects in the overall distribution (e.g. see Johnston 2011), and ultimately for deep enough samples, measurement of the faint-end slope will tell us if LAEs are numerous enough to be the primary sources of reionisation (see Dressler et al 2015 for a discussion).…”

Section: Introductionmentioning

confidence: 99%

MUSE deep-fields: the Ly α luminosity function in the Hubble Deep Field-South at 2.91 < z < 6.64

Drake

Guiderdoni

Blaizot

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We present the first estimate of the Lyα luminosity function using blind spectroscopy from the Multi Unit Spectroscopic Explorer, MUSE, in the Hubble Deep Field South. Using automatic source-detection software, we assemble a homogeneously-detected sample of 59 Lyα emitters covering a flux range of −18.0 < log 10 (F) < −16.3 (erg s −1 cm −2 ), corresponding to luminosities of 41.4 < log 10 (L) < 42.8 (erg s −1 ). As recent studies have shown, Lyα fluxes can be underestimated by a factor of two or more via traditional methods, and so we undertake a careful assessment of each object's Lyα flux using a curve-of-growth analysis to account for extended emission. We describe our self-consistent method for determining the completeness of the sample, and present an estimate of the global Lyα luminosity function between redshifts 2.91 < z < 6.64 using the 1/V max estimator. We find the luminosity function is higher than many number densities reported in the literature by a factor of 2 − 3, although our result is consistent at the 1σ level with most of these studies. Our observed luminosity function is also in good agreement with predictions from semi-analytic models, and shows no evidence for strong evolution between the high-and low-redshift halves of the data. We demonstrate that one's approach to Lyα flux estimation does alter the observed luminosity function, and caution that accurate flux assessments will be crucial in measurements of the faint end slope. This is a pilot study for the Lyα luminosity function in the MUSE deep-fields, to be built on with data from the Hubble Ultra Deep Field which will increase the size of our sample by almost a factor of 10.

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Shedding light on the galaxy luminosity function

Cited by 69 publications

References 385 publications

The redshift distribution of cosmological samples: a forward modeling approach

The redshift distribution of cosmological samples: a forward modeling approach

Forward modeling of spectroscopic galaxy surveys: application to SDSS

MUSE deep-fields: the Ly α luminosity function in the Hubble Deep Field-South at 2.91 < z < 6.64

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