UFig v1: The ultra-fast image generator

Abstract

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

8 pages, 4 figures, accepted for publication in JOSS

References in corpus (20)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• The Hyper Suprime-Cam SSP Survey: Overview and Survey Design
• The Dark Energy Survey: more than dark energy - an overview
• GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
• Measuring Baryon Acoustic Oscillations along the line of sight with photometric redshifs: the PAU survey
• Image Analysis for Cosmology: Results from the GREAT10 Galaxy Challenge
• Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing
• Precise photometric redshifts with a narrow-band filter set: The PAU Survey at the William Herschel Telescope
• Fast Point Spread Function Modeling with Deep Learning
• Modelling the Transfer Function for the Dark Energy Survey
• Calibrated Ultra Fast Image Simulations for the Dark Energy Survey
• The redshift distribution of cosmological samples: a forward modeling approach
• Measurement of the B-band Galaxy Luminosity Function with Approximate Bayesian Computation
• Monte Carlo Control Loops for cosmic shear cosmology with DES Year 1
• The PAU Survey: Measurement of Narrow-band galaxy properties with Approximate Bayesian Computation
• Redshift requirements for cosmic shear with intrinsic alignment
• The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging
• Simulation-based inference of deep fields: galaxy population model and redshift distributions

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