Image simulations of highly magnified clumpy galaxies

TL;DR

ClumPyLen is a versatile Python simulator that creates realistic mock observations of strongly lensed, high-redshift, clumpy galaxies, aiding studies of galaxy evolution and observational detectability of stellar structures.

Contribution

The paper introduces ClumPyLen, a new modular tool for simulating realistic lensed galaxy images with detailed galaxy components and observational effects, supporting diverse scientific applications.

Findings

Simulated images match real observations in morphology and magnitude.

Supports a wide range of instruments and filters, including JWST.

Enables study of clump detectability and blending effects.

Abstract

We present ClumPyLen, a Python-based simulator designed to produce realistic mock observations of strongly lensed, high-redshift, clumpy star-forming galaxies. The tool models galaxy components such as disks, bulges, and spiral arms using S\'ersic profiles, and it populates them with stellar clumps whose properties are sampled from physically motivated distributions. ClumPyLen includes the effects of gravitational lensing through user-provided deflection angle maps and simulates realistic observational conditions by accounting for instrumental effects, Point-Spread-Function convolution, sky background, and photon noise. The simulator can support a wide range of filters and instruments; here we focus on HST/ACS, HST/WFC3-IR, and JWST/NIRCam. We demonstrate the capabilities of the code through two examples, including a detailed simulation of the z = 6.145 source Cosmic Archipelago lensed by MACS J0416.1-2403. The simulated images closely match the morphology and limiting magnitudes of real observations. ClumPyLen is designed to explore the detectability of stellar clumps in terms of mass and size, especially in the low-mass regime, and it allows the study of clump blending effects. Thanks to its modular design, the code is highly adaptable to a wide range of scientific goals, including lensing studies, galaxy evolution, and the generation of synthetic datasets for machine learning or forward modeling applications.