Watson, Chambers, & Smith: A Pragmatic Approach to the Analysis of DNA Histograms with a Definable G1 Peak [Cytometry 8:1-8 (1987)]

Fox: A Model for the Computer Analysis of Synchronous DNA Distributions Obtained by Flow Cytometry [Cytometry 1:71-80 (1980)]

Unfortunately, no algorithms are consistently reliable in fitting all the distributions you may encounter. You may have to experiment with different options and constraints in finding your best results.

You should first apply the models of choice to the data without any parameter constraints.  For most distributions, FlowJo will be able to locate the G1 and G2 peaks, and thereby accurately fit the distribution with the model.  (Of course, some distributions are not easily fit by some of the models; you may have to select an alternative model or an option to the model to obtain a better fit).

When you encounter a difficult-to-fit distribution, you will want to begin constraining parameters.  The first parameter to constrain is usually the G2 peak position (if the G2 peak is hard to find), or the G1 peak position (if the G1 peak is hard to find).  Here you may wish to analyze a control sample that has a good distribution, and set constraining ranges for the peak(s) based on that sample.  When you copy the Cell Cycle analysis to the difficult sample (by drag-and-drop), the ranges will automatically be applied.  FlowJo now constrains the peak positions; this may be sufficient to help it fit the data.

Also consider constraining the peak positions relative to each other. For example, you can define the G2 peak position to be 2x the G1 peak, or the G1 peak to be 0.5x the G2 peak.  By constraining this particular parameter, you make it easier for FlowJo to fit the data.

If the fit still looks awful, consider constraining the CV of the ill-defined peak.  If the CV's for the two peaks are significantly different, then you should probably perform this constraint anyway.  For most analyses, the G2 CV should be equal to the G1 CV; you can set either one to be equal to the other, relieving that parameter from the fitting model.

By judicious use of ranges to constrain peak positions, and relative or absolute values for the peak widths, you should be able to fit nearly any distribution.

Note that the "2-populations" model is very difficult to use. There are 24 different parameters that are fit in the unconstrained model; therefore, it is difficult for FlowJo to generate a well-behaved model without guidance.  If you need to use this model, you should probably constrain at least the positions of the two G1 peaks, and the relative positions of the G2 peak(s), as well as the relative CVs of the G2 peaks.