Setting Up the BEAST

Basics

  1. Define project and grid input parameters in datamodel.py
  2. Execute BEAST Run using python run_beast.py with appropriate task flags
    • Default Full Stack Run: python run_beast.py -p -o -t -f

BEAST Data Model

Before running the BEAST, you will need to modify datamodel.py to specify the required parameters for generating models and fitting data. These parameters (and example values) are described below.

Project Details

  • project: pathname of of working subdirectory.
  • filters: names of photometric filter passbands (matching library names).
  • basefilters: short versions of passband names.
  • obsfile: filename for input flux data.
  • obs_colnames: column names in obsfile for observed fluxes. The input data MUST be in fluxes, NOT in magnitudes and the fluxes MUST be in normalized Vega units.

Artificial Star Test (AST) File Parameters

The BEAST generates artificial star test (AST) input files based on additional input parameters from datamodel.py.

  • ast_models_selected_per_age: number of models to pick per age (default = 70).
  • ast_bands_above_maglimit: number of filters that must be above the magnitude limit for an AST to be included in the list (default = 3).
  • ast_realization_per_model: number of realizations of each included AST model to be put into the list (default = 20).
  • ast_maglimit: two options: (1) number of magnitudes fainter than the 90th percentile faintest star in the photometry catalog to be used for the mag cut (default = 1); (2) custom faint end limits (space-separated list of numbers, one for each band).
  • ast_with_positions: (optional; bool) if True, the AST list is produced with X,Y positions. If False, the AST list is produced with only magnitudes.
  • ast_pixel_distribution: (optional; float) minimum pixel separation between AST position and catalog star used to determine the AST spatial distribution. Used if ast_with_positions is True.
  • ast_reference_image: (optional; string) name of the reference image used by DOLPHOT when running the measured photometry. Required if ast_with_positions is True and no X,Y information is present in the photometry catalog.
  • astfile: pathname to the AST files (single camera ASTs).
  • noisefile : pathname to the output noise model file.

Grid Definition Parameters

The BEAST generates a grid of stellar models based on aditional input parameters from datamodel.py. See <beast_grid_inputs.rst> for details on model libraries.

  • distances: distance grid range parameters. [min, max, step], or [fixed number].
  • distance_unit: specify magnitude (units.mag) or a length unit
  • logt: age grid range parameters (min, max, step).
  • z: metallicity grid points.
  • oiso: isochrone model grid. Current choices: Padova or MIST. Default: PARSEC+CALIBRI: oiso = isochrone.PadovaWeb()
  • osl: stellar library definition. Options include Kurucz, Tlusty, BTSettl, Munari, Elodie and BaSel. You can also generate an object from the union of multiple individual libraries: osl = stellib.Tlusty() + stellib.Kurucz()
  • extLaw: extinction law definition.
  • avs: dust column in magnitudes (A_V) grid range parameters (min, max, step).
  • rvs: average dust grain size grid (R_V) range parameters (min, max, step).
  • fAs: mixture factor between “MW” and “SMCBar” extinction curves (f_A) grid range parameters (min, max, step).
  • *_prior_model: prior model definitions for stellar and dust parameters.
    For more on setting up priors see BEAST priors.

Optional Features

Add additional filters to grid

Define list of filternames as additional_filters and alter add_spectral_properties call:

add_spectral_properties_kwargs = dict(filternames=filters + additional_filters)

Skip verify_params exit

Add noexit=True keyword to verify_input_format() call in run_beast.py:

verify_params.verify_input_format(datamodel, noexit=True)

Remove constant SFH prior

Add prior_kwargs to datamodel.py:

prior_kwargs = dict(constantSFR=False)

Add kwargs defining code block before add_stellar_priors() call in run_beast.py:

if hasattr(datamodel, 'prior_kwargs'):
  prior_kwargs = datamodel.prior_kwargs
else:
  prior_kwargs = {}

Enable Exponential Av Prior

Set av_prior_model in datamodel.py:

av_prior_model = {'name': 'exponential', 'a': 2.0, 'N': 4.0}

BEAST Filters

The filters are defined in beast/libs/filters.hd5. The file contains two groups:

  • content: fields are TABLENAME (string), OBSERVATORY (string), INSTRUMENT (string), NORM (float), CWAVE (float), PWAVE (float), COMMENT (string)
  • filters has a group for each filter, with the same names as TABLENAME. The groups contain a dataset with the fields WAVELENGTH (float array, in Angstroms) and THROUGHPUT (float array).

The filters currently included in the BEAST filter library are as follows.

GROUND_JOHNSON_U
GROUND_JOHNSON_B
GROUND_JOHNSON_V
GROUND_COUSINS_R
GROUND_COUSINS_I
GROUND_BESSELL_J
GROUND_BESSELL_H
GROUND_BESSELL_K
HST_NIC2_F110W
HST_NIC2_F160W
HST_NIC2_F205W
HST_WFPC2_F218W
HST_ACS_HRC_F220W
HST_ACS_HRC_F250W
HST_WFPC2_F255W
HST_WFPC2_F300W
HST_ACS_HRC_F330W
HST_WFPC2_F336W
HST_ACS_HRC_F344N
HST_ACS_HRC_F435W
HST_ACS_WFC_F435W
HST_WFPC2_F439W
HST_WFPC2_F450W
HST_ACS_HRC_F475W
HST_ACS_WFC_F475W
HST_ACS_HRC_F502N
HST_ACS_WFC_F502N
HST_ACS_HRC_F550M
HST_ACS_WFC_F550M
HST_ACS_HRC_F555W
HST_ACS_WFC_F555W
HST_WFPC2_F555W
HST_ACS_HRC_F606W
HST_ACS_WFC_F606W
HST_WFPC2_F606W
HST_WFPC2_F622W
HST_ACS_HRC_F625W
HST_ACS_WFC_F625W
HST_ACS_HRC_F658N
HST_ACS_WFC_F658N
HST_ACS_HRC_F660N
HST_ACS_WFC_F660N
HST_WFPC2_F675W
HST_ACS_HRC_F775W
HST_ACS_WFC_F775W
HST_WFPC2_F791W
HST_ACS_HRC_F814W
HST_ACS_WFC_F814W
HST_WFPC2_F814W
HST_ACS_HRC_F850LP
HST_ACS_WFC_F850LP
HST_WFPC2_F850LP
HST_ACS_HRC_F892N
HST_ACS_WFC_F892N
CFHT_CFH12K_CFH7406
CFHT_CFH12K_CFH7504
CFHT_MEGAPRIME_CFH7605
CFHT_MEGAPRIME_CFH7701
CFHT_MEGAPRIME_CFH7803
CFHT_WIRCAM_CFH8002
CFHT_WIRCAM_CFH8101
CFHT_WIRCAM_CFH8102
CFHT_WIRCAM_CFH8103
CFHT_WIRCAM_CFH8104
CFHT_WIRCAM_CFH8201
CFHT_WIRCAM_CFH8202
CFHT_WIRCAM_CFH8203
CFHT_WIRCAM_CFH8204
CFHT_WIRCAM_CFH8301
CFHT_WIRCAM_CFH8302
CFHT_WIRCAM_CFH8303
CFHT_WIRCAM_CFH8304
CFHT_WIRCAM_CFH8305
CFHT_MEGAPRIME_CFH9301
CFHT_MEGAPRIME_CFH9401
CFHT_MEGAPRIME_CFH9601
CFHT_MEGAPRIME_CFH9701
CFHT_MEGAPRIME_CFH9801
HST_WFC3_F098M
HST_WFC3_F105W
HST_WFC3_F110W
HST_WFC3_F125W
HST_WFC3_F126N
HST_WFC3_F127M
HST_WFC3_F128N
HST_WFC3_F130N
HST_WFC3_F132N
HST_WFC3_F139M
HST_WFC3_F140W
HST_WFC3_F153M
HST_WFC3_F160W
HST_WFC3_F164N
HST_WFC3_F167N
HST_WFC3_F200LP
HST_WFC3_F218W
HST_WFC3_F225W
HST_WFC3_F275W
HST_WFC3_F280N
HST_WFC3_F300X
HST_WFC3_F336W
HST_WFC3_F343N
HST_WFC3_F350LP
HST_WFC3_F373N
HST_WFC3_F390M
HST_WFC3_F390W
HST_WFC3_F395N
HST_WFC3_F410M
HST_WFC3_F438W
HST_WFC3_F467M
HST_WFC3_F469N
HST_WFC3_F475W
HST_WFC3_F475X
HST_WFC3_F487N
HST_WFC3_F502N
HST_WFC3_F547M
HST_WFC3_F555W
HST_WFC3_F600LP
HST_WFC3_F606W
HST_WFC3_F621M
HST_WFC3_F625W
HST_WFC3_F631N
HST_WFC3_F645N
HST_WFC3_F656N
HST_WFC3_F657N
HST_WFC3_F658N
HST_WFC3_F665N
HST_WFC3_F673N
HST_WFC3_F680N
HST_WFC3_F689M
HST_WFC3_F763M
HST_WFC3_F775W
HST_WFC3_F814W
HST_WFC3_F845M
HST_WFC3_F850LP
HST_WFC3_F953N
HST_WFC3_FQ232N
HST_WFC3_FQ243N
HST_WFC3_FQ378N
HST_WFC3_FQ387N
HST_WFC3_FQ422M
HST_WFC3_FQ436N
HST_WFC3_FQ437N
HST_WFC3_FQ492N
HST_WFC3_FQ508N
HST_WFC3_FQ575N
HST_WFC3_FQ619N
HST_WFC3_FQ634N
HST_WFC3_FQ672N
HST_WFC3_FQ674N
HST_WFC3_FQ727N
HST_WFC3_FQ750N
HST_WFC3_FQ889N
HST_WFC3_FQ906N
HST_WFC3_FQ924N
HST_WFC3_FQ937N
HST_NIC3_F108N
HST_NIC3_F110W
HST_NIC3_F113N
HST_NIC3_F150W
HST_NIC3_F160W
HST_NIC3_F164N
HST_NIC3_F166N
HST_NIC3_F175W
HST_NIC3_F187N
HST_NIC3_F190N
HST_NIC3_F196N
HST_NIC3_F200N
HST_NIC3_F205M
HST_NIC3_F212N
HST_NIC3_F215N
HST_NIC3_F222M
HST_NIC3_F240M
CFHT_MEGAPRIME_CFH9702
HST_WFPC2_F170W
GALEX_FUV
GALEX_NUV
GROUND_2MASS_J
GROUND_2MASS_H
GROUND_2MASS_Ks
SPITZER_IRAC_36
SPITZER_IRAC_45
SPITZER_IRAC_58
SPITZER_IRAC_80
WISE_RSR_W1
WISE_RSR_W2
WISE_RSR_W3
WISE_RSR_W4
GROUND_SDSS_U
GROUND_SDSS_G
GROUND_SDSS_R
GROUND_SDSS_I
GROUND_SDSS_Z