- GalSim (GalSim-developers, 2012) is a very widely used tool in wide-field optical cosmology
- The official simulation suite for the LSST Dark Energy Science Collaboration is DC2. It actually uses GalSim under the hood. (LSST Dark Energy Science Collaboration, 2021)
GalSim is an open-source software library to perform image simulations. It can be used either through its python interface, or as an executable, configurable through YAML files. Most of the computation-heavy parts of the code are written in C++ for performance. In its simplest configuration, GalSim uses simple parametric models for both galaxies (e.g., Sersic, exponential) and point spread functions (e.g., Moffat). The former can also be generated from real Hubble Space Telescope images (provided the instrument for which images are to be simulated has a larger point spread function). The latter can be created as the convolution of an optical and an atmospheric point spread function. The optical part can be simulated if the user provides a set of instrument related parameters. Alternatively, an external calibration image of the point spread function can be used. The positions and parameters of the objects to be simulated can be read from a catalog. Several options also exist for noise, detector effects and WCS. The simulated image outputs are usually stored in FITS files. At present, it does not handle simulation of several image artefacts, including trails due to satellites.
3.3.3. TrailSimulate
To assess impacts of satellite trails on science, we need to create simulated images with specific trail properties. This tool will require detailed (probably instrument-dependent, plugin) models of the appearance of trails and related instrumental effects.
A first mode of the tool would be driven by a specific satellite pass prediction.
A second mode might be to give the code a trail occurrence distribution as a function of brightness (e.g., 10 trails per square degree per hour uniformly distributed between magnitudes 4 and 6) rather than a PassPredict output. We will refer to this as the “rate input mode”.
Inputs:
- Transit list from PassPredict run on output from EphemSimulate or (rate input mode) rate of trails as a function of brightness
- Observation parameters, the same as fed to PassPredict
- Simulated image without trails
a) Must be consistent with B.
- Model (code) for generating trails including CCD and optical side effects
Outputs:
- Simulated image with trails
- Fraction of image pixels affected (including by side effects)
Existing software. We are not aware of any existing software that would address this issue.
