loading page

Monte Carlo Method for Calculating Oxygen Abundances and Their Uncertainties from Strong-Line Flux Measurements
  • federica B bianco
federica B bianco

Corresponding Author:[email protected]

Author Profile

Abstract

We present the open-source Python code pyMCZ that determines oxygen abundance and its distribution from strong emission lines in the standard metallicity scales, based on the original IDL code of \citet{kewley02} with updates from \citet{kewley08}, and expanded to include more recently developed scales. The standard strong-line diagnostics have been used to estimate the oxygen abundance in the interstellar medium through various emission line ratios in many areas of astrophysics, including galaxy evolution and supernova host galaxy studies. We introduce a Python implementation of these methods that, through Monte Carlo (MC) sampling, better characterizes the statistical reddening-corrected oxygen abundance confidence region. Given line flux measurements and their uncertainties, our code produces synthetic distributions for the oxygen abundance in up to 13 metallicity scales simultaneously, as well as for E(B-V), and estimates their median values and their 66% confidence regions. In addition, we provide the option of outputting the full MC distributions, and their kernel density estimates. We test our code on emission line measurements from a sample of nearby supernova host galaxies (\(z<0.15\)) and compare our metallicity results with those from previous methods. We show that our metallicity estimates are consistent with previous methods but yield smaller uncertainties. We also offer visualization tools to assess the spread of the oxygen abundance in the different scales, as well as the shape of the estimated oxygen abundance distribution in each scale, and develop robust metrics for determining the appropriate MC sample size. The code is open access and open source and can be found at https://github.com/nyusngroup/pyMCZ