The PAU Survey: a forward modeling approach for narrow-band imaging

Tortorelli, Luca; Bruna, Lorenza Della; Herbel, Jörg; Amara, Adam; Réfrégier, Alexandre; Alarcón, A.; Carretero, J.; Castander, F. J.; Vicente, J. De; Eriksen, Martin; Fernández, E.; Folger, M.; García-Bellido, J.; Gaztañaga, E.; Miquel, R.; Aranda, C. Padilla; Sánchez, E.; Serrano, S.; Stothert, Lee; Tallada, Pau; Tonello, N.

doi:10.1088/1475-7516/2018/11/035

Cited by 18 publications

(29 citation statements)

References 42 publications

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“…[1]). A similar approach has also been performed recently for image simulations with narrow-band photometric data from the Physics of the Accelerating Universe Survey, (PAUS 8 ), as shown in [30].…”

Section: Contentsmentioning

confidence: 96%

“…To simulate galaxy spectra, we need basic galaxy properties as inputs. The model from which such properties are drawn is fully described in [1], and further clarified in [30]. In this section, we review the aspects of the model necessary for simulating galaxy spectra given an input galaxy population.…”

Section: Galaxy Population Modelmentioning

confidence: 99%

“…Red Blue α -0. 1.62165971 1.99463069 Table 1: Model parameters used in this work, as also reported in Table 3 in [30].…”

Section: Luminosity Functionsmentioning

confidence: 99%

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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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Section: Contentsmentioning

confidence: 96%

Section: Galaxy Population Modelmentioning

confidence: 99%

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Forward modeling of spectroscopic galaxy surveys: application to SDSS

Fagioli

Riebartsch

Nicola

et al. 2018

J. Cosmol. Astropart. Phys.

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“…Comparing observed data and simulations on the basis of colours among 40 different narrow-bands is therefore not an efficient option, since it does not allow us to immediately visualise the information about the overall spectral shape. To capture the global information from such a high dimensional dataset, we follow the approach we already adopted in [67], meaning we perform a PCA on the observed-frame PAUS narrow-band spectra. We perform the PCA following the same steps highlighted in section 4.4.…”

Section: Observed-frame Principal Component Analysismentioning

confidence: 99%

Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation

Tortorelli¹,

Fagioli²,

Herbel³

et al. 2020

J. Cosmol. Astropart. Phys.

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Narrow-band imaging surveys allow the study of the spectral characteristics of galaxies without the need of performing their spectroscopic follow-up. In this work, we forward-model the Physics of the Accelerating Universe Survey (PAUS) narrow-band data. The aim is to improve the constraints on the spectral coefficients used to create the galaxy spectral energy distributions (SED) of the galaxy population model in In that work, the model parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data using Approximate Bayesian Computation (ABC). This led to stringent constraints on the B-band galaxy luminosity function parameters, but left the spectral coefficients only broadly constrained. To address that, we perform an ABC inference using CFHTLS and PAUS data. This is the first time our approach combining forwardmodelling and ABC is applied simultaneously to multiple datasets. We test the results of the ABC inference by comparing the narrow-band magnitudes of the observed and simulated galaxies using Principal Component Analysis, finding a very good agreement. Furthermore, we prove the scientific potential of the constrained galaxy population model to provide realistic stellar population properties by measuring them with the CIGALE SED fitting code. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited (i < 22.5) sample of galaxies spanning the redshift range 0 < z < 1.0. We find that properties like stellar masses, star-formation rates, mass-weighted stellar ages and metallicities are in agreement within errors between observations and simulations. Overall, this work shows the ability of our galaxy population model to correctly forward-model a complex dataset such as PAUS and the ability to reproduce the diversity of galaxy properties at the redshift range spanned by CFHTLS and PAUS.

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“…The MCCL method was also used to determine the redshift distribution of cosmological samples of galaxies [21]. Recently, the galaxy population model resulting from the MCCL method derived from broadband imaging data was successfully compared to the Sloan Digital Sky Survey (SDSS) spectroscopic sample [22] and the narrow band imaging Physics of the Accelerating Universe Survey (PAUS) [23].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Kacprzak¹,

Herbel²,

Nicola³

et al. 2020

Phys. Rev. D

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Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum C l and the redshift distribution nðzÞ of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of C l and nðzÞ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of Ω m , σ 8 , and intrinsic alignment amplitude A IA , defined as S 8 D IA ¼ σ 8 ðΩ m =0.3Þ 0.5 D IA , where D IA ¼ 1 − 0.11ðA IA − 1Þ. We find S 8 D IA ¼ 0.895 þ0.054 −0.039 , where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

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The PAU Survey: a forward modeling approach for narrow-band imaging

Cited by 18 publications

References 42 publications

Forward modeling of spectroscopic galaxy surveys: application to SDSS

Forward modeling of spectroscopic galaxy surveys: application to SDSS

Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

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