Setting Up the BEAST¶

Basics¶

Define project and grid input parameters in datamodel.py
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 dust parameters (A_V, R_V, f_A). Default: flat prior.

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

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