base9.yaml

general:
    verbose:            0     # 0 is quiet, 1 is verbose, 2 is very verbose

    files: # This will change to "paths" in a future version
        photFile:       "NGC0104/NGC0104.sampleACS.phot"
        outputFileBase: "NGC0104/NGC0104.v2"
        modelDirectory: "base-models/"
        scatterFile:    ""    # Empty string.  This is primarily a CLI option

    main_sequence:
        msRgbModel:     4     # 0 = Girardi, 1 = Chaboyer-Dotter w/He sampling, 2 = Yale-Yonsei, 3 = Old (jc2mass) DSED, 4 = New DSED

    white_dwarfs:
        wdModel:        1     # 0 = Wood, 1 = Montgomery, 2 = Althaus, 3 = Renedo
        M_wd_up:        8.0   # The maximum mass for a WD-producing star
        ifmr:           1     # 0 = Weidemann, 1 = Williams, 2 = Salaris lin, 3 = Salaris pw lin, 4+ = tunable

    cluster:
        starting:
            Fe_H:        -0.72
            distMod:     13.37809
            Av:          0.104717
            Y:           0.270982
            carbonicity: 0.00
            logAge:      10.130129

        priors:
            means:
                Fe_H:        -0.72
                distMod:     13.37809
                Av:          0.104717
                Y:           0.270982
                carbonicity: 0.00
                logAge:      10.130129

            sigmas:
                Fe_H:        0.00
                distMod:     0.05
                Av:          0.04
                Y:           0.01
                carbonicity: 0.0
                logAge:      .inf


        minMag:         0   # these variables can be used to read a subset of the photometry
        maxMag:         30
        index:          0     # with 0 being the first filter in the dataset

makeIsochrone:
    # No makeIsochrone-specific settings yet

scatterCluster:
     brightLimit:       -10   # apparant mags, can remove bright stars, e.g. RGB
     faintLimit:        30    # apparant mags, can remove faint stars, e.g. faint MS and WDs
     relevantFilt:      0     # 0=bluest band available
     limitS2N:          1     # use to remove objects with overly low signal-to-noise
     crowded:           yes    # Determines which signal to noise coefficients are used
     exposures: # exposure times in hours
         U:       1
         B:       1
         V:       1
         R:       1
         I:       1
         J:       1
         H:       1
         K:       1
         UVf275w: .21
         UVf336w: .083
         UVf438w: .02
         F606W:   .042
         F814W:   .042

makeCMD:
    # No makeCMD-specific settings yet

singlePopMcmc:
    stage2IterMax:      2000
    stage3Iter:         1000
    runIter:            11000
    thin:               1

    # Adaptive burnin parameters
    # To duplicate the pre-adaptive behaviour, set
    # adaptive:
    #     bigStepIter:    0
    #     trialIter:      2000  # Same as burnIter
    #
    # To duplicate big-step only burnin, set
    # adaptive:
    #     bigStepIter:    2000
    #     trialIter:      2000
    adaptive:
        bigStepIter:    100
        trialIter:      100

    stepSizes:
        age:            0.005
        Fe_H:           0.005
        distMod:        0.005
        Av:             0.002
        Y:              0.003
        carbonicity:    0.001
        ifmrIntercept:  0.01
        ifmrSlope:      0.008
        ifmrQuadCoef:   0.008

multiPopMcmc:
    Y_starting: [0.2467, 0.33]
    YA_start:           0.2467
    YB_start:           0.33
    lambda_start:       0.50

    Y_limits:   [0.15, 0.30, 0.45]
    YA_lo:              0.15
    YA_hi:              0.30
    YB_hi:              0.45

    lambdaStep:         0.01

sampleWDMass:
    # No sampleWDMass-specific settings yet

sampleMass:
    deltaMass:          0.0005
    deltaMassRatio:     0.001

simCluster:
    nStars:             100000
    percentBinary:      0     # percent binaries (drawn randomly)
    percentDB:          0     # percent of WDs that have He atmospheres (drawn randomly)
    nFieldStars:        0
    nBrownDwarfs:       0