The estimation and subtraction of low frequency noise in each bolometer (the FLT model) very often causes ringing around bright sources. This ringing can be very bad for the first few iterations but often improves if sufficient iterations are performed. Masking the FLT model consists of excluding known bright sources from the estimation of the low frequency noise, and thus reducing the ringing. The ability to do this using the "flt.xxx" set of configuration parameters has been around for while, but sadly the ability to do automatic FLT masking has not been available - an external mask image needs to be supplied. By comparison, the AST model can be masked automatically using the ast.zero_snr and ast.zero_snrlo parameters. The corresponding parameters flt.zero_snr and flt.zero_snrlo do exist, but do not really work since the SNR values are only available once a map has been made. By then it is to late to do the FLT masking - the FLT model has already been applied and the ringing is therefore already present in the residuals.
But a new parameter called ast.skip can get round this. This new parameter defaults to zero, but if set to a positive integer it gives the number of initial iterations for which the AST model (i.e. astronomical signal) is to be skipped. For example, if ast.skip is set to 5 then the first five iterations will not include an AST model. The map will still be created at the end of each iteration, but it willnot be used to find the expected astronomical signal, and the residuals will be left unchanged. This means that all these first five iterations will start from essentially the same time series data - the initial cleaned raw data. However, what this means is that we can now create an FLT mask automatically from the SNR values in the map, and refine that mask five times. So when we get to the sixth iteration, we have a usable FLT mask and we have not introduced any ringing into the residuals.
It's a bit like doing a separate run of five iterations to determine the FLT mask before starting again to do the main run.
In summary, to use automatic FLT masking, add the following to your config:
ast.skip=5
flt.zero_snr=5
flt.zero_snrlo=3
But a new parameter called ast.skip can get round this. This new parameter defaults to zero, but if set to a positive integer it gives the number of initial iterations for which the AST model (i.e. astronomical signal) is to be skipped. For example, if ast.skip is set to 5 then the first five iterations will not include an AST model. The map will still be created at the end of each iteration, but it willnot be used to find the expected astronomical signal, and the residuals will be left unchanged. This means that all these first five iterations will start from essentially the same time series data - the initial cleaned raw data. However, what this means is that we can now create an FLT mask automatically from the SNR values in the map, and refine that mask five times. So when we get to the sixth iteration, we have a usable FLT mask and we have not introduced any ringing into the residuals.
It's a bit like doing a separate run of five iterations to determine the FLT mask before starting again to do the main run.
In summary, to use automatic FLT masking, add the following to your config:
ast.skip=5
flt.zero_snr=5
flt.zero_snrlo=3
If you use a fixed number of iterations, you may want to increase your numiter value by the ast.skip value, so that you are doing the same number of real iterations. As usual, you can play around with the precise values of these parameters.
FLT masking speeds up convergence as shown in the following plot of normalised change in the map against iteration number - red uses the new scheme described above, green uses a fixed external FLT mask, and blue is without FLT masking.
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