alfalpha: A Python Adaptation of the 'alf' Code for Spectroscopic Modeling of Stellar Populations

alfalpha is a Python-based adaptation of the alf spectroscopic modeling tool, designed to measure the chemical properties of stellar populations with high precision. This tool is specifically tailored for analyzing the absorption line spectra of old stellar systems (ages > 1 Gyr) across a broad range of metallicities, making it a valuable resource for studies of galaxy evolution and stellar populations.

Building on the foundational work of the original alf code (Conroy+18), alfalpha incorporates Single Stellar Population (SSP) models based on the MILES and IRTF stellar libraries, the MIST isochrones, and ATLAS theoretical response functions. These response functions enable the modeling of variations in individual elemental abundances, allowing for detailed chemical abundance measurements of elements like Fe, Mg, and others. This capability makes alfalpha particularly powerful for reconstructing the chemical enrichment histories of galaxies and stellar systems.

A key advantage of the Python adaptation is its seamless integration with modern statistical and modeling tools, including MCMC sampling via emcee, and dynamic nested sampling via dynesty, allowing efficient exploration of high-dimensional parameter spaces. Despite the shift from Fortran to Python, alfalpha maintains competitive performance thanks to these advanced and efficient modeling techniques, with convergence times comparable to the original Fortran implementation.

Key Features (similar to alf, but now in Python):

• Models absorption line spectra using theoretical age- and metallicity-dependent response functions for up to 19 elements.
• Accounts for key uncertainties in stellar evolution, telluric absorption, and sky line residuals.
• Capable of fitting stellar populations spanning metallicities from [Fe/H] ≈ -2.0 to +0.3, suitable for globular clusters to massive galaxy populations.
• Operates in continuum-normalized space to sidestep challenges related to flux calibration and dust attenuation.
• Python-based implementation, compatible with modern libraries and analysis pipelines.

Dependencies:

• emcee (Foreman-Mackey et al. 2013): Ensemble sampler for MCMC fitting.
• dynesty (Speagle 2020): Dynamic nested sampling for high-dimensional parameter exploration.
• MPI: Multi-processor parallelization for efficient computation.
• Python 3.x and associated scientific libraries (e.g., NumPy, SciPy, Matplotlib, h5py, AstroPy)

Future development:

• IMF variation modeling: The ability to constrain and explore initial mass function (IMF) variations in stellar populations, improving insights into star formation physics.
• Incorporation of extended star formation histories (SFH): Support for non-simple stellar populations by modeling complex, extended star formation histories over time.
• Integration of continuum modeling: Future updates aim to account for continuum features, enabling the inclusion of photometric and flux-calibrated data for broader applications.
• Expansion to younger stellar populations: Development of additional models to analyze systems younger than 1 Gyr, broadening the range of applicable stellar populations.
• Improved stellar libraries: Plans to incorporate updated stellar libraries, namely the alpha-enhanced models from Park+24

This repository is for the paper "Carbon and Iron Deficiencies in Quiescent Galaxies at z=1-3 from JWST-SUSPENSE: Implications for the Formation Histories of Massive Galaxies" by Aliza Beverage. It is associated with the manuscript number #AAS56394.