FAQ
Frequently Asked Questions
Q: What is GalfitS?
A: GalfitS is a powerful, open-source tool for detailed galaxy decomposition. It allows for simultaneous fitting of a galaxy’s morphological components (like bulge, disk, bar) and their spectral energy distributions (SEDs) across multiple wavelength bands. It is built on JAX for high performance.
Q: What are the main features of GalfitS?
A: - Multi-band Image Fitting: Simultaneously model galaxy structure across different wavelengths. - SED Modeling: Fit physical parameters of stellar populations (age, metallicity, dust) and AGNs. - Flexible Models: Use various profiles (Sersic, Ferrer, etc.) and combine them. - Performance: Leverages JAX for JIT compilation and GPU/TPU acceleration. - Advanced Optimization: Includes multiple fitting backends like jaxopt, dynesty (nested sampling), and evolutionary strategies. - Extensible: Easily add new models or priors.
Q: How do I install GalfitS?
A: Please refer to the Installation guide for detailed instructions. The key steps are installing JAX with the appropriate backend (CPU or GPU) and then installing the required Python packages.
Q: I’m getting an error related to JAX. What should I do?
A: Make sure you have installed the correct version of JAX for your system (CPU, NVIDIA GPU, or Apple Silicon). The installation commands can be found in the Installation guide. If you have a GPU, ensure your drivers are up to date.
Q: How does the configuration file work?
A: The configuration file (e.g., quickstart.lyric) is the heart of a GalfitS run. It’s a text file where you define everything: - The target galaxy (name, coordinates, redshift). - The input data (images, PSFs, masks, spectra). - The model components (e.g., a Sersic profile for a bulge, an exponential disk). - The initial parameters for each component, including their bounds and whether they are fixed or free.
A detailed explanation can be found in the Introduction to GalfitS Config Files section.
Q: Can GalfitS fit spectra as well?
A: Yes. GalfitS can perform spectrum-only fitting or a combined fitting of images and spectra. This is useful for detailed studies of stellar populations and AGN properties. See the Examples of Config Files for an example.
Q: What is the difference between `optimizer`, `dynesty`, and `ES` fitting methods?
A: They are different algorithms for finding the best-fit parameters: - optimizer: A fast, gradient-based method (like Adam) that finds a single best-fit solution (a local or global minimum of the chi-square). It’s good for quick exploration. - dynesty: A nested sampling method. It is more computationally expensive but provides full posterior distributions for all parameters and calculates the Bayesian evidence, which is useful for model comparison. - ES (Evolutionary Strategy): A population-based optimization method that is robust against getting stuck in local minima. It’s a good alternative to gradient-based optimizers.
Q: How can I contribute to GalfitS?
A: GalfitS is an open-source project, and contributions are welcome. You can contribute by reporting bugs, suggesting new features, improving the documentation, or submitting pull requests with new code on the GitHub repository.