stardate

stardate is a tool for measuring precise stellar ages. It combines isochrone fitting with gyrochronology (rotation-based age inference) to increase the precision of stellar ages on the main sequence. The best possible ages provided by stardate will be for stars with rotation periods, although ages can be predicted for stars without rotation periods too. If you don’t have rotation periods for any of your stars, you might consider using isochrones.py as stardate is simply an extension to isochrones that incorporates gyrochronology. stardate reverts back to isochrones when no rotation period is provided.

In order to get started you can create a dictionary containing the observables you have for your star. These could be atmospheric parameters (like those shown in the example below for the Sun), or just photometric colors, like those from 2MASS, SDSS or Gaia. If you have a parallax, asteroseismic parameters, or an idea of the maximum V-band extinction you should throw those in too. Set up the star object and stardate.Star.fit() will run Markov Chain Monte Carlo (using emcee) in order to infer a Bayesian age for your star.

Example usage

import stardate as sd

# Create a dictionary of observables
iso_params = {"teff": (5777, 10),     # Teff with uncertainty.
              "logg": (4.44, .05),    # logg with uncertainty.
              "feh": (0., .001),      # Metallicity with uncertainty.
              "parallax": (1., .01),  # Parallax in milliarcseconds.
              "B": (15.48, 0.02),     # You must provide at least one magnitude.
              "maxAV": .1}            # Maximum extinction

prot, prot_err = 26, 1

# Set up the star object.
star = sd.Star(iso_params, prot=prot, prot_err=prot_err)  # Here's where you add a rotation period

# Run the MCMC
star.fit()

# Print the median age with the 16th and 84th percentile uncertainties.
age, errp, errm, samples = star.age_results()
print("stellar age = {0} + {1} + {2}".format(age, errp, errm))

>> stellar age = 4.5 + 2.1 - 1.3

License & attribution

Copyright 2018, Ruth Angus.

The source code is made available under the terms of the MIT license.

If you make use of this code, please cite this package and its dependencies. You can find more information about how and what to cite in the citation documentation.