A way forward for Cosmic Shear: Monte-Carlo Control Loops

Réfrégier, Alexandre; Amara, Adam

doi:10.1016/j.dark.2014.01.002

Cited by 28 publications

(40 citation statements)

References 18 publications

(21 reference statements)

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“…This allows us to forward model a spectroscopic galaxy sample, and to compare it to an existing one after having performed the same cuts on both. The forward modeling approach is becoming a widely used technique [1,28,29], and consists of generating observable quantities from an astrophysical model containing, for instance, the evolution of the luminosity functions of red and blue galaxies with cosmic time (see e.g. [1]).…”

Section: Contentsmentioning

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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Section: Contentsmentioning

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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“…Instead, image simulations have often been used to determine the shear calibration (e.g. Zuntz et al 2013;Miller et al 2013;Fenech Conti et al 2017;Refregier & Amara 2014;Jee et al 2016). These simulations must include all the relevant details of the real universe in order to provide an accurate calibration.…”

Section: Introductionmentioning

confidence: 99%

Practical Weak-lensing Shear Measurement with Metacalibration

Sheldon

Huff

2017

ApJ

271

323

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Metacalibration is a recently introduced method to accurately measure weak gravitational lensing shear using only the available imaging data, without need for prior information about galaxy properties or calibration from simulations. The method involves distorting the image with a small known shear, and calculating the response of a shear estimator to that applied shear. The method was shown to be accurate in moderate sized simulations with galaxy images that had relatively high signal-to-noise ratios, and without significant selection effects. In this work we introduce a formalism to correct for both shear response and selection biases. We also observe that, for images with relatively low signal-to-noise ratios, the correlated noise that arises during the metacalibration process results in significant bias, for which we develop a simple empirical correction. To test this formalism, we created large image simulations based on both parametric models and real galaxy images, including tests with realistic point-spread functions. We varied the point-spread function ellipticity at the five percent level. In each simulation we applied a small, few percent shear to the galaxy images. We introduced additional challenges that arise in real data, such as detection thresholds, stellar contamination, and missing data. We applied cuts on the measured galaxy properties to induce significant selection effects. Using our formalism, we recovered the input shear with an accuracy better than a part in a thousand in all cases.

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“…In recent years, forward modeling has become an increasingly popular method to calibrate shear measurements. A consistent framework for this is the Monte-Carlo Control Loops (MCCL) scheme [20][21][22][23], originally developed for wide-field imaging data. This method typically requires simulating a large amount of synthetic images with realistic properties.…”

Section: Introductionmentioning

confidence: 99%

Fast point spread function modeling with deep learning

Herbel

Kacprzak

Amara

et al. 2018

J. Cosmol. Astropart. Phys.

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Modeling the Point Spread Function (PSF) of wide-field surveys is vital for many astrophysical applications and cosmological probes including weak gravitational lensing. The PSF smears the image of any recorded object and therefore needs to be taken into account when inferring properties of galaxies from astronomical images. In the case of cosmic shear, the PSF is one of the dominant sources of systematic errors and must be treated carefully to avoid biases in cosmological parameters. Recently, forward modeling approaches to calibrate shear measurements within the Monte-Carlo Control Loops (MCCL) framework have been developed. These methods typically require simulating a large amount of wide-field images, thus, the simulations need to be very fast yet have realistic properties in key features such as the PSF pattern. Hence, such forward modeling approaches require a very flexible PSF model, which is quick to evaluate and whose parameters can be estimated reliably from survey data. We present a PSF model that meets these requirements based on a fast deep-learning method to estimate its free parameters. We demonstrate our approach on publicly available SDSS data. We extract the most important features of the SDSS sample via principal component analysis. Next, we construct our model based on perturbations of a fixed base profile, ensuring that it captures these features. We then train a Convolutional Neural Network to estimate the free parameters of the model from noisy images of the PSF. This allows us to render a model image of each star, which we compare to the SDSS stars to evaluate the performance of our method. We find that our approach is able to accurately reproduce the SDSS PSF at the pixel level, which, due to the speed of both the model evaluation and the parameter estimation, offers good prospects for incorporating our method into the MCCL framework.

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A way forward for Cosmic Shear: Monte-Carlo Control Loops

Cited by 28 publications

References 18 publications

Forward modeling of spectroscopic galaxy surveys: application to SDSS

Forward modeling of spectroscopic galaxy surveys: application to SDSS

Practical Weak-lensing Shear Measurement with Metacalibration

Fast point spread function modeling with deep learning

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