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 catalogDEC
: declination from photometry catalogfield
: field in the brickinside_brick
: inside the brick boundariesinside_chipgap
: in ACS chip gapFluxes
: units are ergs/(cm^2 s A)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 PDFPmax_indx
: index in BEAST model grid corresponding toPmax
specgrid_indx
: index in spectroscopic grid corresponding toPmax
chi2min
: minimum value of chisqrchi2min_indx
: index in BEAST model grid corresponding tochi2min
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 PDFX_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 magnitudesRv
: R(V) = A(V)/E(B-V) = ratio of total to selective extinctionf_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 metallicityM_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 magnituderadius
: 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 fluxlogHST_WFC3_F275W_wd
: log10 of the extinguished WFC3 F275W fluxlogHST_WFC3_F336W_nd
: log10 of the unextinguished WFC3 F336W fluxlogHST_WFC3_F336W_wd
: log10 of the extinguished WFC3 F336W fluxlogHST_ACS_WFC_F475W_nd
: log10 of the unextinguished ACS F475W fluxlogHST_ACS_WFC_F475W_wd
: log10 of the extinguished ACS F475W fluxlogHST_ACS_WFC_F814W_nd
: log10 of the unextinguished ACS F814W fluxlogHST_ACS_WFC_F814W_wd
: log10 of the extinguished ACS F814W fluxlogHST_WFC3_F110W_nd
: log10 of the unextinguished WFC3 F110W fluxlogHST_WFC3_F110W_wd
: log10 of the extinguished WFC3 F110W fluxlogHST_WFC3_F160W_nd
: log10 of the unextinguished WFC3 F160W fluxlogHST_WFC3_F160W_wd
: log10 of the extinguished WFC3 F160W fluxlogGALEX_FUV_nd
: log10 of the unextinguished GALEX FUV fluxlogGALEX_FUV_wd
: log10 of the extinguished GALEX FUV fluxlogGALEX_NUV_nd
: log10 of the unextinguished GALEX FUV fluxlogGALEX_NUV_wd
: log10 of the extinguished GALEX FUV fluxlogF_UV_6_13e_nd
: log10 of the unextinguished flux between 6 and 13 eVlogF_UV_6_13e_wd
: log10 of the extinguished flux between 6 and 13 eVlogF_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)