Forward modeling of spectroscopic galaxy surveys: application to SDSS

Fagioli, Martina; Riebartsch, Julian; Nicola, Andrina; Herbel, Jörg; Amara, Adam; Réfrégier, Alexandre; Chang, C.; Gamper, Laurenz; Tortorelli, Luca

doi:10.1088/1475-7516/2018/11/015

Cited by 14 publications

(15 citation statements)

References 103 publications

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“…It is worth to notice that the UFig galaxy population in the first PC shows a flatter shape and a more pronounced flux in the bluer wavelength with respect to the PAUS observed one. It is remarkable to observe that we obtain the same results found by [11], but using a photometric dataset, which in principle contains less spectral information than spectroscopic one about the underlying galaxy population. [11] justify their result to the presence of an overall bluer population of galaxies in UFig basis spectra with respect to their SDSS sample.…”

Section: Pcasupporting

confidence: 81%

“…It is remarkable to observe that we obtain the same results found by [11], but using a photometric dataset, which in principle contains less spectral information than spectroscopic one about the underlying galaxy population. [11] justify their result to the presence of an overall bluer population of galaxies in UFig basis spectra with respect to their SDSS sample. Our results confirm that this is the case and the conclusion can be extended to the PAUS sample too.…”

Section: Pcasupporting

confidence: 81%

See 1 more Smart Citation

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

Tortorelli

Bruna

Herbel

et al. 2018

J. Cosmol. Astropart. Phys.

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Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In [1], a calibration method based on forward modeling, called MCCL, was proposed. This relies on fast image simulations (e.g., UFig) [2] that capture the key features of galaxy populations and measurement effects. The MCCL approach has been used in [3] to determine the redshift distribution of cosmological galaxy samples and, in the process, the authors derived a model for the galaxy population mainly based on broad-band photometry. Here, we test this model by forward modeling the 40 narrow-band photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the same forced photometric pipeline on data and simulations using Source Extractor [4]. The image simulation scheme performance is assessed at the image and at the catalogues level. We find good agreement for the distribution of pixel values, the magnitudes, in the magnitude-size relation and the interband correlations. A principal component analysis is then performed, in order to derive a global comparison of the narrow-band photometry between the data and the simulations. We use a 'mixing' matrix to quantify the agreement between the observed and simulated sets of Principal Components (PCs). We find good agreement, especially for the first three most significant PCs. We also compare the coefficients of the PCs decomposition. While there are slight differences for some coefficients, we find that the distributions are in good agreement. Together, our results show that the galaxy population model derived from broad-band photometry is in good overall agreement with the PAUS data. This offers good prospect for incorporating spectral information to the galaxy model by adjusting it to the PAUS narrow-band data using forward modeling.

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Section: Pcasupporting

confidence: 81%

Section: Pcasupporting

confidence: 81%

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

Tortorelli

Bruna

Herbel

et al. 2018

J. Cosmol. Astropart. Phys.

Self Cite

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“…In this section, we provide a brief description of the galaxy population model we adopt in this work and of the image simulator UFig. A more thorough description can be found in [1,3,65] (see in particular figure 1 of [1]). UFig [49,50] is a code developed to simulate wide-field optical astronomical images.…”

Section: Galaxy Population Modelmentioning

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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“…There are numerous scientific uses of SDSS data, for example, cosmology measurements of dark energy and basic astronomy studies of stars, galaxies, and quasars. SDSS has enabled many science discoveries such as the development of cosmological models that help to describe the history and the future of the universe, 26,27 the study of the growth of black holes, 28 the analysis of galaxies, 29 and so on. Over 7700 peer‐reviewed publications in astronomy and other sciences have used and were made possible by SDSS data.…”

Section: Introductionmentioning

confidence: 99%

Assessing data change in scientific datasets

Müller

Faybishenko

Agarwal

et al. 2021

Concurrency and Computation

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Scientific datasets are growing rapidly and becoming critical to next-generation scientific discoveries. The validity of scientific results relies on the quality of data used and data are often subject to change, for example, due to observation additions, quality assessments, or processing software updates. The effects of data change are not well understood and difficult to predict. Datasets are often repeatedly updated and recomputing derived data products quickly becomes time consuming and resource intensive and may in some cases not even be necessary, thus delaying scientific advance.Despite its importance, there is a lack of systematic approaches for best comparing data versions to quantify the changes, and ad-hoc or manual processes are commonly used. In this article, we propose a novel hierarchical approach for analyzing data changes, including real-time (online) and offline analyses. We employ a variety of fast-to-compute numerical analyses, graphical data change representations, and more resource-intensive recomputations of a subset of the data product. We illustrate the application of our approach using three scientific diverse use cases, namely, satellite, cosmological, and x-ray data. The results show that a variety of data change metrics should be employed to enable a comprehensive representation and qualitative evaluation of data changes. K E Y W O R D Sdata management, data versions, hierarchical data change analysis, QA/QC, scientific data change analysis INTRODUCTIONNext-generation scientific discoveries are increasingly relying on processing of data from experiments, observations and simulations. The validity of scientific results relies on the quality of data. However, many scientific communities experience "data change." 1 Data are often published in the form of versions. A new version of a dataset may mean, for example, that new entries have been added to the dataset (time series or survey data), a new processing software has been used for quality assessment and control of the data, or the settings of a measurement device have been found to be incorrect and the data taken from the device and previously published had to be corrected. 2The need to take into account data changes can have huge implications on compute resources and researcher's time for reprocessing, storage requirements for managing multiple versions of the data, and the science results obtained from using the data. For example, in the environmental sciences, satellite data from the moderate resolution imaging spectroradiometer (MODIS) is used to calculate evapotranspiration (ET). 3 ET is an

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Forward modeling of spectroscopic galaxy surveys: application to SDSS

Cited by 14 publications

References 103 publications

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

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

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

Assessing data change in scientific datasets

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