BEAMS/BEAMS.params at master · djones1040/BEAMS · GitHub

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# BEAMS example parameter file

[inputdata]
# file that gives info about BEAMS free parameters
mcmcparamfile = mcmcparams.input

# input file in FITRES format (see example data)
fitresfile = exampledata/PS1_SIM_example.FITRES
outfile = exampledata/PS1_BEAMS_example.out

# P(Ia) column in fitres file
piacol = PNN_Ia
# column with spec-confirmed SNe having value = 1, others = 0
specconfcol = SNSPEC

# another way to specify spec/phot SNe by using the IDSURVEY
# keyword in FITRES file
specidsurvey = 53,5,50,61,62,63,64,65,66
photidsurvey = 15

# if non-zero, the max # of SNe to include
nsne = 0

# photometric SNe don't contribute any value at
# low-z, so they can often be excluded based on the
# photidsurvey keyword
zminphot = 0.08

[sim]
# options to test BEAMS when using simulated data

# if true, remove sim SNe with bad redshifts based on
# the SIM_ZCMB keyword in the FITRES file
nobadzsim = False
# if true, remove SNe with SIM_TYPE_INDEX != 1 and bad
# redshifts from the bunch
onlyIa = False
# remove all SNe Ia with correct redshifts
onlyCC = False
# if non-zero, this test allows BEAMS to use the traditional method of
# making a cut on P(Ia) and treating all SNe with
# P(Ia) > pcutval as bona fide SNe Ia.
pcutval = 0
nspecsne = 0

[models]
# if true, use two gaussian and skewed gaussian models
# for the CC SN population
twogauss = False
skewedgauss = False
# different CC SN parameters at each control point
zCCdist = False

[lightcurve]
crange = -0.3,0.3
x1range = -3,3
fitprobmin = 0.001
x1errmax = 1
pkmjderrmax = 2
zmin = 0.01
zmax = 1.0

# these nuisance parameters are only used
# to remove SNe with NaN uncertainties
salt2alpha = 0.161
salt2beta = 3.060
salt2alphaerr = 0.006
salt2betaerr = 0.063
sigint = 0

# cutwin options have format variable, min, max
# used to restrict range of additional parameters
cutwin =

# if true, uses x1 and c parameters to define ellipse cut
# instead of a box cut
x1cellipse = False

[mass]
# these options allow BEAMS to simultaneously
# fit for the host mass bias (e.g. Sullivan+2010).
# These have not yet been tested.
masscorr = False
masscorrfixed = False
masscorrmag = 0.07
masscorrmagerr = 0.023
masscorrdivide = 10

[mcmc]
# emcee options - see their documentation
nthreads = 8
nwalkers = 200
nsteps = 1500
ninit = 1500
# if ntemps > 0, uses emcee's parallel-tempered
# ensemble sampler with specified number of
# temperatures
ntemps = 0
# random step size for initializing MCMC
mcrandstep = 1e-4

# minimization parameters for scipy.optimize.minimize
# these are ignored if ntemps > 0
minmethod = L-BFGS-B
miniter = 1

# sometimes the minimizer does good enough for the MCMC,
# even if it says it fails.  To force it to succeed, set
# forceminsuccess = 1
forceminsuccess = False

# number of log(z) bins.  Must match MCMC parameter file
nbins = 25
equalbins = False

[bootstrap]
# options for taking bootstrap subsets from
# a big FITRES file.  Number of low-z SNe is
# fixed atm.

# if mcsubset = 1, take bootstrap subsets
mcsubset = False
# random seed
mcrandseed = 0
# size of non-low-z SNe
subsetsize = 1000
# number of MC samples
nmc = 25
# start at this sample
nmcstart = 1
# file with low-z SNe
mclowz =
# number of low-z SNe to include
lowzsubsetsize = 250