Estimate Options

Estimate Options#

There are several options that we can use to change or improve a given estimation.

Before we get into these, let’s initialize our target IRAS 04171+2756 as a Target. For more information about Target objects, see the Target Metadata Tutorial.

from stelpar import Target

target = Target('IRAS 04171+2756')

Choosing an Isochrone Model#

There are three isochrone models that are built into the code, two of which are derived from the Dartmouth Stellar Evolution Program (DSEP; Chaboyer et al. 2001; DOI: 10.1086/323872; Dotter et al. 2008, DOI: 10.1086/589654). The first is a non-magnetic DSEP model and the second is a DSEP-based model with the enhancement of magnetic field physics (Feiden & Chaboyer 2012; DOI: 10.1088/0004-637X/761/1/30). The third model is from the PAdova and TRieste Stellar Evolution Code (PARSEC; Bressan et al. 2012, DOI: 10.1111/j.1365-2966.2012.21948.x).

To change the isochrone model, we pass one of 'std' (DSEP-non-magnetic), 'mag' (DSEP-magnetic), or 'parsec' (PARSEC) to the isochrone keyword argument of an Estimate instance. The default is 'mag'.

from stelpar import Estimate

est = Estimate(target, isochrone='std')

Changing the Interpolation Method#

There are three built-in interpolation methods available for interpolating the model grid to a given (age, mass) pair. These can be changed using the interp_method keyword argument in an Estimate instance.

  • interp_method='true': bilinear interpolation using DFInterpolator (see docs) from the isochrones python package (Morton 2015; bibcode: 2015ascl.soft03010M)

  • interp_method='nearest': nearest-neighbor finds the closest datapoint to the given age and mass

  • interp_method='hybrid': finds the nearest-neighbor datapoint in age then linearly interpolates in mass

If the keywords are either 'nearest' or 'hybrid', stelpar uses a pre-interpolated version of the isochrone model where the grid spacing is much smaller than the original model. The default is 'true'.

est = Estimate(target, isochrone='std', interp_method='hybrid')

loading isochrone model grid ..

Extinction Options#

There are two optional changes we can make to the built-in extinciton calculation that is run at every iteration of the MCMC.

The first is to set \(A_V=0\) and remove it entirely as a fit parameter (i.e., the calculation never happens). This is useful if we know that our target is nearby and not surrounded by gas and dust (e.g., the star is \(\sim\)30 pc away and does not have a protoplanetary disk nor exist in a star-forming region).

To set \(A_V=0\) we pass zero_extinction=True to Estimate (the default is False).

est = Estimate(target, isochrone='std', interp_method='hybrid', zero_extinction=True)

loading isochrone model grid ..

When extinction is calculated it is done so using an identical procedure to the synphot python package (STScI Development Team 2018; bibcode: 2018ascl.soft11001S), except using numpy arrays and numba instead of the built-in synphot object framework. This makes the calculation significantly faster. If you prefer using the pure-synphot method, you can pass use_synphot=True to Estimate (the default is False).

est = Estimate(target, isochrone='std', interp_method='hybrid', zero_extinction=False, use_synphot=True)

loading isochrone model grid ..

Using Different Moves#

Built into the Target infrastructure is the ability to use different ensemble moves. A good description of what these are and their basic usage can be found in the emcee moves tutorial. All of the available moves and their references in the literature can be found in the emcee moves documentation of the emcee documentation.

stelpar gives the ability to use four of these moves: StretchMove, DEMove, DESnookerMove, and KDEMove, all from emcee. They can be accessed via the keywords "stretch", "DE", "snooker", and "KDE", respectively. Along with a keyword, a weight must be applied to each move. Any arguments can also be given to each move function as you would with emcee.

StretchMove with a weight of 1.0 is the default move.

For example, suppose we want to use 20% DEMove and 80% DESnookerMove:

target.moves = [
    ('snooker', 0.8), 
    ('DE', 0.2),
]

target.moves

[(<emcee.moves.de_snooker.DESnookerMove at 0x1ed2dba7260>, 0.8),
 (<emcee.moves.de.DEMove at 0x1ed2da32930>, 0.2)]
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