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STAM

Stellar-Track-based Assignment of Mass (STAM) is a python package that assigns mass, metallicity, or age, to stars based on their location on the Hertzsprung-Russell diagram (color-magnitude diagram), using stellar evolutionary tracks (not provided by this package).

The package has been tested for Gaia DR2 sub-solar main-sequence stars, using PARSEC evolutionary tracks. See Hallakoun & Maoz 2021 for details (please cite this paper if you use STAM in your publication).

Getting started

Prerequisites

  • python 3.6 or above
  • astropy
  • configparser
  • numpy
  • scipy
  • tdqm
  • geomdl

Installing

Create and activate a conda environment with the necessary modules:

$ conda create -n stam astropy configparser numpy scipy python=3.7.1
$ source activate stam

Install the stam package:

$ pip install git+https://github.com/naamach/stam.git

Upgrading

To upgrade stam run:

$ pip install git+https://github.com/naamach/stam.git --upgrade

Using stam

Download the PARSEC models

First, you need to download some stellar evolution tracks, that will be used to estimate the stellar parameters. You can download the PARSEC isochrones from here. Make sure to download isochrones covering the full range of parameters you wish to probe (i.e. spanning the full relevant range of age, mass, and metallicity, in small enough steps). For the analysis in Hallakoun & Maoz 2021 we downloaded the following PARSEC tracks:

  • Ages 1-400Myr in 1Myr steps, [M/H]=0.7 (for the pre-main-sequence tracks)
  • Ages 0.5-15Gyr in 0.5Gyr steps, [M/H]=-2 to +0.7 in 0.1dex steps (for the main-sequence tracks)

You might need to download the isochrones iteratively, in case your query exceeds 400 rows. Save all the resulting *.dat files into a single folder. stam will concatenate all the *.dat files in that folder into a single table.

Download the Gaia data

You can query the Gaia DR2 database directly from the Gaia Archive, or any other method of your preference. Save the resulting table in *.FITS format (it works faster for large tables).

Prepare the configuration file

You will have to provide stam with a config.ini file in the working directory (the directory from which you run the script). The file should look like that (see config.ini.example in the main directory):

; config.ini
[LOG]
PATH = /path/to/log/file/
CONSOLE_LEVEL = INFO ; DEBUG, INFO, WARNING, ERROR, CRITICAL
FILE_LEVEL = DEBUG ; DEBUG, INFO, WARNING, ERROR, CRITICAL

[GENERAL]
SAVE = TRUE ; save results to file?
PATH = /path/to/working/directory/ ; result file destination path
OUTPUT_TYPE = csv ; npy, csv
CSV_FORMAT = .8f ; CSV format (without "%")

[GAIA]
PATH = /path/to/gaia/files/ ; path to Gaia data folder
FILE = GaiaFileName.fits ; Gaia data file name (only works for FITS files)
CORRECT_EXTINCTION = TRUE ; correct Gaia data for extinction?

[BINARY]
CONSIDER_TWINS = TRUE ; consider equal-mass binary sequence when assigning mass/metallicity/age?
FLUX_RATIO_MIN = 1.9 ; minimal binary twin flux ratio
FLUX_RATIO_MAX = 2 ; maximal binary twin flux ratio

[MODELS]
SOURCE = PARSEC ; which isochrone models to use?
M_MIN = 0.15 ; [Msun] minimum track mass
M_MAX = 1 ; [Msun] maximum track mass
M_STEP = 0.05 ; [Msun] track mass step
AGE = 5 ; [Gyr] age of the MS tracks
MH_PRE_MS = 0.7 ; pre-MS track [M/H]
SMOOTH = True ; smooth track?
SMOOTH_SIGMA = 3 ; Gaussian smoothing sigma
EXCLUDE_PRE_MS_MASSES = ; [Msun] exclude the pre-MS tracks of these masses (to avoid crossing other tracks),
                                                       ; separate values by a comma, leave empty to include all

[PARSEC]
PATH = /path/to/PARSEC/files/ ; path to the PARSEC *.dat tables (concatenates all *.dat files in the folder to a single table)

[INTERP]
METHOD = rbf ; rbf, griddata, nurbs
RBF_FUN = linear

[MASS]
N_REALIZATIONS = 10 ; number of realizations

[MH]
N_REALIZATIONS = 10 ; number of realizations

Alternatively, you can use stam.utils.write_config to help you write the configuration file (consult the function help for details).

Running stam

Without applying special treatment to equal-mass binaries

To assign mass and metallicities to all the Gaia sources in your Gaia data file, run in python (if the config keyword CONSIDER_TWINS is set to FALSE):

from stam.run import  get_mass_and_metallicity

m_mean, m_error, mh_mean, mh_error = get_mass_and_metallicity()

This returns, for each star, the mean values of the mass (m_mean) and metallicity (mh_mean), and their corresponding standard deviations (*_error).

This will also save the results to files, according to the specifications in the config.ini file. A log file will be saved to the same folder.

Optional input keywords for get_mass_and_metallicity:

  • idx: select only specific rows in the Gaia table (default: None)
  • suffix: add a customized suffix to the output file names (default: None)
  • config_file: specify which configuration file to use (default: config.ini)
  • sample_settings: a dictionary including keywords "vmin", "vmax", and "dist" (default: None). If provided, only Gaia sources within the specific transverse velocities (in km/s) and distance (in pc), will be evaluated.

When applying special treatment to equal-mass binaries

If you want to treat the equal-mass binary sequence above the main sequence separately, set the config keyword CONSIDER_TWINS to TRUE. To assign mass and metallicities to all the Gaia sources in your Gaia data file, run in python:

from stam.run import  get_mass_and_metallicity

m_mean, m_error, binary_m_mean, binary_m_error, m_weight,\
 mh_mean, mh_error, binary_mh_mean, binary_mh_error, mh_weight = get_mass_and_metallicity()

This time you will get some extra output variables:

  • *_mean: the mass/metallicity mean value assuming a single star
  • *_error: the mass/metallicity standard deviation assuming a single star
  • binary_*_mean: the mass/metallicity mean value assuming an equal-mass binary
  • binary_*_error: the mass/metallicity standard deviation assuming an equal-mass binary
  • *_weight: the single-star probability = the fraction of realizations in which the star was outside of the defined equal-mass binary region

Choosing the interpolation method

STAM includes three different interpolation methods, that can be used to interpolate the evolutionary track grid: rbf, griddata, and nurbs. It is recommended to plot the resulting grid before running the full mass assignment procedure, to check for problems in the grid evaluation. For example, to check the rbf linear interpolation:

import numpy as np
import scipy
import matplotlib.pyplot as plt
import stam

isochrone = stam.getmodels.read_parsec()
mass = np.arange(0.15, 1.1, 0.05)
tracks = stam.gentracks.get_combined_isomasses(isochrone, mass=mass, age=5, mh_pre_ms=0.7, age_min=0.005,
                                                is_smooth=True, smooth_sigma=3, exclude_pre_ms_masses=[0.15])

x = np.array(tracks["bp_rp"])
y = np.array(tracks["mg"])
z = np.array(tracks["mass"])

xstep=0.05
ystep=0.05

def tracks2grid(tracks, xparam="bp_rp", yparam="mg", xstep=0.05, ystep=0.05):

    xmin = np.min(np.around(tracks[xparam], -int(np.round(np.log10(xstep)))))
    xmax = np.max(np.around(tracks[xparam], -int(np.round(np.log10(xstep)))))
    ymin = np.min(np.around(tracks[yparam], -int(np.round(np.log10(ystep)))))
    ymax = np.max(np.around(tracks[yparam], -int(np.round(np.log10(ystep)))))
    x, y = np.meshgrid(np.arange(xmin, xmax, xstep), np.arange(ymin, ymax, ystep))
            
    return x, y, xmin, xmax, ymin, ymax

grid_x, grid_y, xmin, xmax, ymin, ymax = tracks2grid(tracks, xstep=xstep, ystep=ystep)

fun_type = "linear"

interp = scipy.interpolate.Rbf(x, y, z, function=fun_type)
grid_z = interp(grid_x, grid_y)

fig = plt.figure()
plt.plot(x, y, 'ko', markersize=1)
plt.imshow(grid_z, origin="lower", vmin=0.1, vmax=1.2, extent=[xmin, xmax, ymin, ymax], cmap='viridis')
plt.colorbar()
plt.title(fun_type)
plt.gca().invert_yaxis()

plt.show()

rbf

This is scipy's radial basis function (RBF) interpolation. See the scipy.interpolate.Rbf reference page for details. When using the rbf method, you should also provide the RBF_FUN keyword, which is the function argument of scipy.interpolate.Rbf. We found the rbf method with linear function to work best in our case (Hallakoun & Maoz 2021).

griddata

This is based on scipy's griddata linear interpolation: scipy.interpolate.griddata, but uses a faster implementation, based on this answer. In our case, it resulted in some artifacts when using combined pre-main-sequence+main-sequence evolutionary tracks.

nurbs

This is geomdl's NURBS library (Non-Uniform Rational Basis Spline).

Acknowledgements

The multicolor plot functions defined in colorline.py are taken from David P. Sanders' colorline Jupyter Notebook.

Citing and attributing

If you use STAM in your work, please provide a link to this webpage, and cite Hallakoun & Maoz 2021:

@ARTICLE{2021MNRAS.507..398H,
       author = {{Hallakoun}, Na'ama and {Maoz}, Dan},
        title = "{A bottom-heavy initial mass function for the likely-accreted blue-halo stars of the Milky Way}",
      journal = {\mnras},
     keywords = {methods: statistical, Hertzsprung-Russell and colour-magnitude diagrams, stars: luminosity function, mass function, stars: statistics, solar neighbourhood, Galaxy: stellar content, Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics},
         year = 2021,
        month = oct,
       volume = {507},
       number = {1},
        pages = {398-413},
          doi = {10.1093/mnras/stab2145},
archivePrefix = {arXiv},
       eprint = {2009.05047},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.507..398H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}