StreamSim: Stellar Stream Simulation & Observation

StreamSim is a Python package for stellar stream data generation and observation simulation. It provides tools to:

• Generate mock stellar stream data from parametric models. The goal is to provide a modular codebase that can be easily configured and run. The user should be able to generate a variety of stream morphologies by changing the configuration files.
• Assign photometric properties to stars using stellar isochrones
• Simulate realistic survey observations with observational effects

StreamSim bridges theoretical/dynamical stream models and realistic mock observations as they would appear in astronomical surveys like LSST.

Note: This package is not intended to generate dynamic models or N-body simulations, but rather to study statistical realizations of parametrized stream geometries.

Documentation

Full documentation: https://lsstdesc.github.io/stream_sim/

• About - Overview and use cases
• Quickstart - Get started in minutes
• Installation - Detailed installation guide
• API Reference - Complete API documentation

Installation

Installation consists of three steps:

# 1. Clone the repository
git clone https://github.com/LSSTDESC/stream_sim.git
cd stream_sim

# 2. Set environment variables
export PYTHONPATH=${PWD}:${PYTHONPATH}
export PATH=${PWD}/bin:${PATH}

# 3. Download required data files
python bin/download_data.py

The code requires common scientific Python packages:

• numpy, scipy, pandas, matplotlib
• astropy, healpy, gala
• ugali (for stellar isochrones)

For full setup (dependencies, optional tools, troubleshooting), see the Installation Guide.
If data download fails or you want details on data structure, see the Data page.

Eventually stream_sim will be installable through common package managers (i.e., pip and/or conda).

Use Cases

StreamSim is particularly useful for:

• Dynamical simulations: Convert N-body simulation outputs into observable quantities
• Algorithm development: Generate test data for stream detection algorithms
• Survey planning: Predict stream detectability in upcoming surveys
• Pipeline validation: Test analysis workflows with known ground truth

See the Quickstart Guide for some examples.

Citation

See the Citation page for more information.