Observations have shown that stars with the same age and mass can have a wide range of rotation periods. The purpose of this code is to compute the posterior probability for a star's age given its observed rotation period and effective temperature. This is achieved by marginalizing over all possible ages that might explain the observed stellar properties with a parametric model that has been fitted to not only the mean rotation periods, but also the intrinsic scatter in observed open cluster rotation sequences.

The documentation is hosted at gyro-interp.readthedocs.io. A minimal example to get you started is below. The method is described in detail in Bouma, Palumbo & Hillenbrand (2023).

Preferred installation method is through PyPI:

pip install gyrointerp

Given a star's rotation period, effective temperature, and uncertainties, what is the gyrochronological age posterior over a grid spanning 0 to 2.6 Gyr?

  import numpy as np
  from gyrointerp import gyro_age_posterior

  # units: days
  Prot, Prot_err = 11, 0.2

  # units: kelvin
  Teff, Teff_err = 4500, 100

  # uniformly spaced grid between 0 and 2600 megayears
  age_grid = np.linspace(0, 2600, 500)

  # calculate the age posterior - takes ~30 seconds
  age_posterior = gyro_age_posterior(
      Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid
  )

  # calculate dictionary of summary statistics
  from gyrointerp import get_summary_statistics
  result = get_summary_statistics(age_grid, age_posterior)

  print(f"Age = {result['median']} +{result['+1sigma']} -{result['-1sigma']} Myr.")

If you were interested in a slower-rotating star that might be closer to 4 Gyr old, which is the oldest age out to which gyro-interp is calibrated, you could modify the following lines:

  age_grid = np.linspace(0, 5000, 500)

  age_posterior = gyro_age_posterior(
      Prot, Teff, Prot_err=Prot_err, Teff_err=Teff_err, age_grid=age_grid, 
      verbose=False, bounds_error='4gyrextrap'
  )

⚠️ Please do not use this code to try to infer ages of stars older than 4 Gyr. This is gyrochronology by interpolation. The extrapolation model invoked by using bounds_error='4gyrextrap' has no physical content beyond 4 Gyr.

The documentation contains more extensive examples, as well as discussion of important caveats.

If you use the code in your work, please reference

@ARTICLE{2023ApJ...947L...3B,
       author = {{Bouma}, Luke G. and {Palumbo}, Elsa K. and {Hillenbrand}, Lynne A.},
        title = "{The Empirical Limits of Gyrochronology}",
      journal = {\apjl},
     keywords = {Stellar ages, Stellar rotation, Field stars, Bayesian statistics, 1581, 1629, 2103, 1900, Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = 2023,
        month = apr,
       volume = {947},
       number = {1},
          eid = {L3},
        pages = {L3},
          doi = {10.3847/2041-8213/acc589},
archivePrefix = {arXiv},
       eprint = {2303.08830},
 primaryClass = {astro-ph.SR},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJ...947L...3B},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

If your result is particularly dependent on the rotation data from any one cluster, we also encourage you to refer to the relevant study:

• α Per: Boyle & Bouma (2023)
• Pleiades: Rebull et al. (2016)
• Blanco-1: Gillen et al. (2020)
• Psc-Eri: Curtis et al. (2019a)
• NGC-3532: Fritzewski et al. (2022)
• Group-X: Messina et al. (2022)
• Praesepe: Rampalli et al. (2021)
• NGC-6811: Curtis et al. (2019b)
• NGC-6819: Meibom et al. (2015)
• Ruprecht-147 Curtis et al. (2020)
• M67: Barnes et al. (2016), Dungee et al (2022), and Gruner et al (2023).