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With the rise of simulation-based inference (SBI) methods, simulations need to be fast as well as realistic. $\texttt{UFig v1}$ is a public Python package that generates simulated astronomical images with exceptional speed - taking approximately the same time as source extraction. This makes it particularly well-suited for simulation-based inference (SBI) methods where computational efficiency is crucial. To render an image, $\texttt{UFig}$ requires a galaxy catalog and a description of the point spread function (PSF). It can also add background noise, sample stars using the Besan\c{c}on model of the Milky Way, and run $\texttt{SExtractor}$ to extract sources from the rendered image. The extracted sources can be matched to the intrinsic catalog, flagged based on $\texttt{SExtractor}$ output and survey masks, and emulators can be used to bypass the image simulation and extraction steps. A first version of $\texttt{UFig}$ was presented in Berg\'e et al. (2013) and the software has since been used and further developed in a variety of forward modelling applications., Comment: 8 pages, 4 figures, submitted to JOSS

We present a combined cosmic shear analysis of the modeling of line-of-sight distortions on strongly lensed extended arcs and galaxy shape measurements in the COSMOS field. We develop a framework to predict the covariance of strong lensing and galaxy shape measurements of cosmic shear on the basis of the small scale matter power-spectrum. The weak lensing measurement is performed using data from the COSMOS survey calibrated with a cloning scheme using the Ultra Fast Image Generator UFig (Berge 2013). The strong lensing analysis is performed by forward modeling the lensing arcs with a main lensing deflector and external shear components from the same Hubble Space Telescope imaging data set. With a sample of three strong lensing shear measurements we present a 2-sigma detection of the cross-correlation signal between the two complementary measurements of cosmic shear along the identical line of sight. With large samples of lenses available with the next generation ground and space based observatories, the covariance of the signal of the two probes with large samples of lenses allows for systematic checks, cross-calibration of either of the two measurement and the measurement of the small scale shear power-spectrum., Comment: 27 pages, 7 figures, 4 tables

Weak Gravitational Lensing is a powerful probe of the dark sector of the Universe. One of the main challenges for this technique is the treatment of systematics in the measurement of cosmic shear from galaxy shapes. In an earlier work, Refregier & Amara (2014) have proposed the Monte Carlo Control Loops (MCCL) to overcome these effects using a forward modeling approach. We focus here on one of the control loops in this method, the task of which is the calibration of the shear measurement. For this purpose, we first consider the requirements on the shear systematics for a given survey and propagate them to different systematics terms. We use two one-point statistics to calibrate the shear measurement and six further one-point statistics as diagnostics. We also propagate the systematics levels that we estimate from the one-point functions to the two-point functions for the different systematic error sources. This allows us to assess the consistency between the systematics levels measured in different ways. To test the method, we construct synthetic sky surveys with an area of 1,700 deg$^2$. With some simplifying assumptions, we are able to meet the requirements on the shear calibration for this survey configuration. Furthermore, we account for the total residual shear systematics in terms of the contributing sources. We discuss how this MCCL framework can be applied to current and future weak lensing surveys., Comment: 23 pages, 4 figures, prepared for submission to JCAP

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 4-band 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., Comment: 26 pages, 10 figures

We present a comprehensive method for determining stellar mass functions, and apply it to samples in the local Universe. We combine the classical 1/Vmax approach with STY, a parametric maximum likelihood method and SWML, a non-parametric maximum likelihood technique. In the parametric approach, we are assuming that the stellar mass function can be modelled by either a single or a double Schechter function and we use a likelihood ratio test to determine which model provides a better fit to the data. We discuss how the stellar mass completeness as a function of z biases the three estimators and how it can affect, especially the low mass end of the stellar mass function. We apply our method to SDSS DR7 data in the redshift range from 0.02 to 0.06. We find that the entire galaxy sample is best described by a double Schechter function with the following parameters: $\log (M^{*}/M_\odot) = 10.79 \pm 0.01$, $\log (\Phi^{*}_1/\mathrm{h^3\ Mpc^{-3}}) = -3.31 \pm 0.20$, $\alpha_1 = -1.69 \pm 0.10$, $\log (\Phi^{*}_2/\mathrm{h^3\ Mpc^{-3}}) = -2.01 \pm 0.28$ and $\alpha_2 = -0.79 \pm 0.04$. We also use morphological classifications from Galaxy Zoo and halo mass, overdensity, central/satellite, colour and sSFR measurements to split the galaxy sample into over 130 subsamples. We determine and present the stellar mass functions and the best fit Schechter function parameters for each of these subsamples., Comment: accepted for publication in MNRAS, see Table 4 and Figure 11 for an overview, 43 pages, 33 figures

We measure the projected density profile, shape and alignment of the stellar and dark matter mass distribution in 11 strong-lens galaxies. We find that the projected dark matter density profile - under the assumption of a Chabrier stellar initial mass function - shows significant variation from galaxy to galaxy. Those with an outermost image beyond $\sim 10$ kpc are very well fit by a projected NFW profile; those with images within 10 kpc appear to be more concentrated than NFW, as expected if their dark haloes contract due to baryonic cooling. We find that over several half-light radii, the dark matter haloes of these lenses are rounder than their stellar mass distributions. While the haloes are never more elliptical than $e_{dm} = 0.2$, their stars can extend to $e_* > 0.2$. Galaxies with high dark matter ellipticity and weak external shear show strong alignment between light and dark; those with strong shear ($\gamma \gtrsim 0.1$) can be highly misaligned. This is reassuring since isolated misaligned galaxies are expected to be unstable. Our results provide a new constraint on galaxy formation models. For a given cosmology, these must explain the origin of both very round dark matter haloes and misaligned strong-lens systems., Comment: 16 pages, 7 figures, 4 tables. Accepted for publication by MNRAS

Weak lensing by large-scale structure is a powerful technique to probe the dark components of the universe. To understand the measurement process of weak lensing and the associated systematic effects, image simulations are becoming increasingly important. For this purpose we present a first implementation of the $\textit{Monte Carlo Control Loops}$ ($\textit{MCCL}$; Refregier & Amara 2014), a coherent framework for studying systematic effects in weak lensing. It allows us to model and calibrate the shear measurement process using image simulations from the Ultra Fast Image Generator (UFig; Berge et al. 2013). We apply this framework to a subset of the data taken during the Science Verification period (SV) of the Dark Energy Survey (DES). We calibrate the UFig simulations to be statistically consistent with DES images. We then perform tolerance analyses by perturbing the simulation parameters and study their impact on the shear measurement at the one-point level. This allows us to determine the relative importance of different input parameters to the simulations. For spatially constant systematic errors and six simulation parameters, the calibration of the simulation reaches the weak lensing precision needed for the DES SV survey area. Furthermore, we find a sensitivity of the shear measurement to the intrinsic ellipticity distribution, and an interplay between the magnitude-size and the pixel value diagnostics in constraining the noise model. This work is the first application of the $\textit{MCCL}$ framework to data and shows how it can be used to methodically study the impact of systematics on the cosmic shear measurement., Comment: 14 pages, 9 Figures, submitted to ApJ