PyStaff: Stellar Absorption Feature Fitting in python

PyStaff (Python Stellar Absorption Feature Fitting) is a python code to recover stellar population parameters (such as Stellar Age, Metallicity and the Initial Mass Function) from visible and near-IR spectra of nearby galaxies. It's built to work with the newest version of the single stellar population models of Conroy, Villaume, van Dokkum & Lind (described here) which are available on request from Charlie Conroy's website.

The code has been used in Vaughan et. al 2018b, to measure the stellar population of NGC 1399 using data from MUSE.

You can read the docs at pystaff.readthedocs.io

Features:

• Recover the best fit recession velocity and velocity dispersion of the spectrum (with higher order moments to be added in the future)
• Measure stellar population parameters such as Age, [Z/H] and the IMF, as well as abundances of the 18 elements varied within the models
• Subtract a series of sky spectra during the fit, to deal with over/under subtracted night sky emission lines
• Carefully account for a varying instrumental resolution as a function of wavelength
• Simultaneously fit a number of emission lines with the stellar absorption features
• Easily switch on or off which parameters to vary, thanks to the lmfit package
• Full sampling of the posterior with emcee
• A simple example of use with an MPI pool is given, to allow the code to be used on a cluster

Using the code on a different set of SSP models would be possible but would require a fair amount of work. It's something I'd like to make easier in future!

The code is being actively documented and updated.

Acknowledgements

A number of functions are imported from Michele Cappellari's fantastic Penalized Pixel Fitting code (pPXF: more info here, and available from PyPI). I'd also like to thank Charlie Conroy for making available the latest version of the SSP models.

Dependencies

• Python 3
• Standard scientific python stack (numpy, scipy, matplotlib, astropy)
• pPXF- pip install ppxf

You can also run pip install -r requirements.txt