Data to reproduce:

This repository holds the codes and data products to reproduce the figures and the open-source aspects of the analysis of the paper Rosenfield et al 2017 (MATCH and PARSEC are closed-source, only their data products are available here).

This repo is designed to reproduce the figures from the paper. The full grid of core overshooting tracks is available elsewhere.

Code:

• codebase: graphics and analysis used in the paper. For an intermediate python user, these codes can be generalized to other tracks in the repo, or forked/used for other projects.

Data products:

• Photometry related (for each cluster) (readme)
  • Reduced HST observations: .gst catalogs
  • Artificial star tests files
  • ASteCA membership probabilities calculated from the .gst catalogs
• Stellar model related (readme)
  • Part of the PARSEC V1.2S core overshooting grid
  • Synthetic stellar populations created from MATCH.fake for use in the paper (readme)
• Files used to create posterior distribution functions (readme)
  • likelihood files from synthetic stellar populations
  • likelihood files from 70% membership probability catalogs (for each cluster)

Making the Figures from the Paper

Figure 1 and 2 are not included. To make the rest of the figures run bash all_plots.sh

Figure-by-Figure

• Figure 3: CMDs of the clusters.
  • data: phot/obs/*gst and phot/asteca/*dat
  • main code: code/cmd_plots.cmd_plots
  • make the figure: python -m clusters_data.code.cmds
• Figure 4: The effect of core overshooting on H and He burning lifetimes as a function stellar mass.
  • data: stev/tracks/track_summary.dat
  • main code: cov_plots.cov_complifetimes
  • make the figure: python -m clusters_data.code.covs -l
• Figure 5: HRD and CMD of tracks as a function of core overshooting
  • data: stev/tracks/...
  • main code: cov_plots.plot_compare_tracks
  • make the figure: python -m clusters_data.code.covs -t
• Figure 6: CMDs of synthetic stellar populations (mock data)
  • data: mock/*full
  • main code: cov_plots.fake_cmds
  • make the figure: python -m clusters_data.code.covs -m
• Figure 7+: PDF plots (marginalized or joint-marginalized)
  • data:
    • Fig 7, 8: pdfs/mock/
    • Fig 9+: pdfs/clusters/
  • main code: pdf_plots.cluster_result_plots
  • make the figures:
    • Fig 7, 8: python -m clusters_data.code.pdfs -t
    • Fig 9, 10: python -m clusters_data.code.pdfs -m
    • Fig 11, 12: python -m clusters_data.code.pdfs