BEAST Output Files

Below are details regarding the output files produced by the BEAST:

  • *_stats.fits: Statistics for each of the fitted and derived parameters, including the 16th/50th/84th percentiles, mean, and expectation value

  • *_pdf1d.fits: Marginalized 1D PDFs for each of the fitted and derived parameters

  • *_pdf2d.fits: Marginalized 2D PDFs for pairs of parameters

  • *_lnp.hd5: Sparsely sampled log likelihoods

  • *_beast_info.asdf: Information about the run saved as an ASDF file

    (e.g. prior models)

Several of the BEAST output files are saved in the hdf5 format, which can be more challenging to access than fits files. There are functions in tools/read_beast_data.py to facilitate reading those files.

Statistics file

Data Parameters

  • ID Name: IAU suggested naming scheme used (example: PHAT J113759.63+421022.03)

  • RA: right ascension from photometry catalog

  • DEC: declination from photometry catalog

  • field: field in the brick

  • inside_brick: inside the brick boundaries

  • inside_chipgap: in ACS chip gap

  • Photometry: listed as flux (not mag), units are normalized Vega fluxes (e.g., flux/flux_vega)

    • HST_WFC3_F275W

    • HST_WFC3_F336W

    • HST_ACS_WFC_F475W

    • HST_ACS_WFC_F814W

    • HST_WFC3_F110W

    • HST_WFC3_F160W

Goodness-of-fit metrics

  • Pmax: maximum probability of nD PDF

  • Pmax_indx: index in BEAST model grid corresponding to Pmax

  • specgrid_indx: index in spectroscopic grid corresponding to Pmax

  • chi2min: minimum value of chisqr

  • chi2min_indx: index in BEAST model grid corresponding to chi2min

Fitted and derived parameters

Each parameter (listed below) has five values associated with it:

  • X_Best: best fit value [“traditional” values]

  • X_Exp: expectation value (average weighted by 1D PDF) [best when not using uncertainties]

  • X_p50: 50th percentile from 1D PDF

  • X_p16: 16th percentile from 1D PDF (p50-p16 is proxy for -1 sigma)

  • X_p84: 84th percentile from 1D PDF (p84-p50 is proxy for +1 sigma)

Dust Parameters

First 3 primary, others derived

  • Av = A(V): visual extinction in magnitudes

  • Rv: R(V) = A(V)/E(B-V) = ratio of total to selective extinction

  • f_A: fraction in extinction curve from A component (MW)

  • Rv_A: R(V)_A = R(V) of A component of BEAST R(V)-f_A model of extinction curves

Stellar Parameters

First 3 primary, others derived

  • M_ini: initial stellar mass (in solar masses)

  • logA: log10 of the stellar age (in years)

  • Z: stellar metallicity

  • M_act: current stellar mass (in solar masses)

  • logL: log10 of the stellar luminosity (in solar luminosities)

  • logT: log10 of the stellar effective temperature (in Kelvin)

  • logg: log10 of the stellar surface gravity (cm s^-2)

  • mbol: bolometric magnitude

  • radius: stellar radius (in solar radii)

Predicted Fluxes

The fitting process also predicts fluxes, both in the observed bands and in other bands of interest.

  • logHST_WFC3_F275W_nd: log10 of the unextinguished WFC3 F275W flux

  • logHST_WFC3_F275W_wd: log10 of the extinguished WFC3 F275W flux

  • logHST_WFC3_F336W_nd: log10 of the unextinguished WFC3 F336W flux

  • logHST_WFC3_F336W_wd: log10 of the extinguished WFC3 F336W flux

  • logHST_ACS_WFC_F475W_nd: log10 of the unextinguished ACS F475W flux

  • logHST_ACS_WFC_F475W_wd: log10 of the extinguished ACS F475W flux

  • logHST_ACS_WFC_F814W_nd: log10 of the unextinguished ACS F814W flux

  • logHST_ACS_WFC_F814W_wd: log10 of the extinguished ACS F814W flux

  • logHST_WFC3_F110W_nd: log10 of the unextinguished WFC3 F110W flux

  • logHST_WFC3_F110W_wd: log10 of the extinguished WFC3 F110W flux

  • logHST_WFC3_F160W_nd: log10 of the unextinguished WFC3 F160W flux

  • logHST_WFC3_F160W_wd: log10 of the extinguished WFC3 F160W flux

  • logGALEX_FUV_nd: log10 of the unextinguished GALEX FUV flux

  • logGALEX_FUV_wd: log10 of the extinguished GALEX FUV flux

  • logGALEX_NUV_nd: log10 of the unextinguished GALEX FUV flux

  • logGALEX_NUV_wd: log10 of the extinguished GALEX FUV flux

  • logF_UV_6_13e_nd: log10 of the unextinguished flux between 6 and 13 eV

  • logF_UV_6_13e_wd: log10 of the extinguished flux between 6 and 13 eV

  • logF_QION_nd: log10 of the unextinguished ionizing flux (*do not use for PHAT results - incorrect*)

  • logF_QION_wd: log10 of the extinguished ionizing flux (*do not use for PHAT results - incorrect*)

1D PDF file

Each extension in the fits file is for one of the parameters listed above. It contains an array with dimensions (N_obs+1, N_bin), where N_obs is the number of stars and N_bin is the number of bins for that parameter. Each entry in the array is the probability (NOT logarithmic) in each bin. The bin values are listed in the last line of the array.

Below is an example for Rv in the phat_small example.

>>> from astropy.io import fits 
>>> hdu = fits.open('beast_example_phat_pdf1d.fits') 
>>> hdu.info() 
Filename: beast_example_phat_pdf1d.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
0  PRIMARY       1 PrimaryHDU       6   (2, 2)   float64
1  Av            1 ImageHDU         8   (11, 270)   float64
2  M_act         1 ImageHDU         8   (50, 270)   float64
3  M_ini         1 ImageHDU         8   (50, 270)   float64
4  Rv            1 ImageHDU         8   (5, 270)   float64
5  Rv_A          1 ImageHDU         8   (9, 270)   float64
6  Z             1 ImageHDU         8   (5, 270)   float64
...
>>> hdu['Rv'].data[0,:]  # 1D PDF for star 0 
array([0.00000000e+00, 9.99753477e-01, 2.46523236e-04, 0.00000000e+00,
     0.00000000e+00])
>>> hdu['Rv'].data[-1,:]  # corresponding bin values 
array([2., 3., 4., 5., 6.])

2D PDF file

Each extension in the fits file is for one of the pairs of fitting parameters (the default is the 7 main parameters, but the user may have selected a different set). The saved arrays have dimensions (N_obs+2, N_bin_1, N_bin_2), where N_obs is the number of stars, N_bin_1 is the number of bins for the first parameter, and N_bin_2 is the number of bins for the second parameter. The last two slices contain the bin values.

Below is an example of the Rv and f_A 2D PDF in the phat_small example.

>>> from astropy.io import fits 
>>> hdu = fits.open('beast_example_phat_pdf2d.fits') 
>>> hdu.info() 
Filename: beast_example_phat_pdf2d.fits
No.    Name      Ver    Type      Cards   Dimensions   Format
0  PRIMARY       1 PrimaryHDU       6   (2, 2)   float64
1  Av+M_ini      1 ImageHDU         9   (50, 11, 271)   float64
2  Av+Rv         1 ImageHDU         9   (5, 11, 271)   float64
3  Av+Z          1 ImageHDU         9   (5, 11, 271)   float64
4  Av+f_A        1 ImageHDU         9   (4, 11, 271)   float64
5  Av+logA       1 ImageHDU         9   (5, 11, 271)   float64
6  M_ini+Rv      1 ImageHDU         9   (5, 50, 271)   float64
7  M_ini+Z       1 ImageHDU         9   (5, 50, 271)   float64
...
>>> hdu['Rv+f_A'].data[0,:,:]  # 2D PDF for star 0 
array([[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
       [6.86784697e-01, 2.94159452e-01, 1.88093274e-02, 0.00000000e+00],
       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.46523236e-04],
       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])
>>> hdu['Rv+f_A'].data[-2,:,:]  # corresponding Rv bin values 
array([[2., 2., 2., 2.],
       [3., 3., 3., 3.],
       [4., 4., 4., 4.],
       [5., 5., 5., 5.],
       [6., 6., 6., 6.]])
>>> hdu['Rv+f_A'].data[-1,:,:]  # corresponding f_A bin values 
array([[0.25, 0.5 , 0.75, 1.  ],
       [0.25, 0.5 , 0.75, 1.  ],
       [0.25, 0.5 , 0.75, 1.  ],
       [0.25, 0.5 , 0.75, 1.  ],
       [0.25, 0.5 , 0.75, 1.  ]])

Log Likelihood file

(to be added)