/Main-Sequence-Variability

Code and data supporting paper: Stochastic modeling of star-formation histories I: the scatter of the star-forming main sequence (2019MNRAS.487.3845C)

Primary LanguageJupyter NotebookMIT LicenseMIT

Main-Sequence-Variability

The main file is the Main_Sequence_Variability.ipynb notebook. It has several examples

  • overview of the autocorrelation functions and its connection with the parameters of the power spectrum density (high-frequency slope and timescale of the break)
  • overview of the analytical results of the measured width of the Main Sequence, as a function of the parameters of the power spectrum density
  • Figure 6 from the paper, showing the relation between the intrinsic Main Sequence and measured Main Sequence, in a toy model
  • Analytical result for the Figure 7 from the manuscript, showing how we recover the parameters of the PSD from observations, in a toy model.

Dependencies are pandas, tqdm and DELCgen (https://github.com/samconnolly/DELightcurveSimulation).

Screenshot of the notebook is below:

Overview of the notebook

Other interesting files are:

  • MS_Variability.py - module with definitions for Main_Sequence_Variability.ipynb

  • ACFTableLargeNov10.csv - tabulated auto-correlation functions, used in the analytical analysis

  • CreateACFTableFlatten.nb - code used to create tabulated auto-correlation functions

For the numerical analysis see: https://github.com/stacchella/variability_SFHs