SMAUG

Table of Contents

• About
• Getting Started
  • Prerequisites
  • Installation
• Contact
• Acknowledgements

About

This is a pipeline for measuring Mn stellar abundances in dwarf galaxies from medium-resolution DEIMOS spectra.

Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

Here are the main Python packages required and the versions used:

• astropy (4.0)
• matplotlib (3.2.1)
• numpy (1.18.2)
• pandas (1.0.3)

You will also need to install MOOG (the moog17scat version from Alex Ji).

Installation

Follow the usual steps to clone the repo:

git clone https://github.com/mdlreyes/SMAUG.git

Wrap the FORTRAN code smooth_gauss to Python using the following:

f2py -c -m smooth_gauss smooth_gauss.f

How the code works

Here are the main parts of the Mn measurement pipeline:

• Code to correct the observed spectra

  • continuum_division.py: gets rid of slowly-varying continuum in observed spectrum
    • get synthetic spectrum (no [Mn/H] specified) from Ivanna’s grid
    • divide obs/synth
    • fit spline to quotient, mask out Mn regions (and other "bad" regions)
    • divide obs spectrum by spline
  • wvl_corr.py: does empirical wavelength correction using Balmer lines

• Code to make synthetic spectra

  • interp_atmosphere.py: given atmospheric params and [Mn/H], create *.atm file for MOOG
  • run_moog.py: use MOOG to make synthetic spectrum - run 8 times, one for each line list!
    • make *.par file
    • specify linelist
    • specify *.atm file
    • splice all pieces of spectrum together into array

• Code to do the fitting

  • match_spectrum.py: prep the observed and synthetic spectra for fitting
    • open observed spectrum
    • interpolate and smooth synth spectrum to match wavelength array & resolution of observed spectrum
  • chi_squared.py: actually fit the observed spectrum with synthetic spectra
    • calculate chi-square value
    • minimize chi-square ([Mn/H] is free parameter)
  • output_mn.py: run chi_squared.py on all stars in a folder
  • output_mn_mpi.py: same as output_mn.py, but using multiprocessing to make it faster

Contact

Mia de los Reyes - @MiaDoesAstro - mdelosre@caltech.edu

Project Link: https://github.com/mdlreyes/SMAUG

Acknowledgements

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