Seasonal Gap

For multi-season campaigns, the unavoidable seasonal gaps might bias the reverberation mapping analysis. mica2 can cope with seasonal gaps by excluding the regions in parameter space that might be connected with gaps. Those regions are defined as

\[|\tau_k - \tau_{\rm gap} - n\times 365.25| < \omega_{\rm gap},~~~~ \omega_k < \omega_{\rm gap},\]

where \(n\) is any integer, \(\tau_k\) and \(\omega_k\) are the time lag and width of \(k\)-th component, \(\tau_{\rm gap}\) and \(\omega_{\rm gap}\) are the central time lag resulting from gaps and gap width, respectively. By default, mica2 sets

\[\tau_{\rm gap} = 365.25/2,\]

and \(\omega_{\rm gap}\) is the assigned as the mean gap width of the data.

To turn on this functionality, edit the option in the parameter file:

FlagGap                 0                  # whether include seasonal gap
                                           # 0: no; 1: yes.
                                           # default: 0

If the default values are not satifactory, edit the option:

#StrGapPrior            [182.6:140.0]      # gap priors if the default priors are not good enough
                                           # valid when FlagGap == 1
                                           # format: [gap_center_set1:gap_width_set1:gap_center_set2:gap_width_set2...]
                                           # gap_center_set1: gap center for 1st dataset (+n*year will also be included)
                                           # gap_width_set1:  gap width for 1st dataset
                                           # default: None

In the Python version, use the arguments as

model = pymica.gmodel()
model.setup(data=data_input, ..., flag_gap=True)

or input the desired gap information as

model = pymica.gmodel()
model.setup(data=data_input, ..., flag_gap=True, gap_prior=[[182.625, 100],])

where gap_prior is a list and specifies the central time lag (182.625 day) and width (100 day) of gaps for all datasets.