Thanks for checking it through John.

We've also been taking another look ourselves and there are a few small changes that we're thinking to make, which we should run by you first:

1. We asked Ao to test this routine with the (very complicated) Dpipi model and it didn't quite find the maximum. It got close but undershot by a few percent. As such, we were thinking to increase the safety factor on line 2299 of LauIsobarDynamics.cc from 1.03 to 1.10. Unless you have some ideas about how to improve the search algorithm? The only thing I can think of would be to do fits at m_0 +/- Gamma_0 as well as at m_0. Not sure if that will really improve things that much, however. Will also add a printout of the value found by the search.
2. We are thinking to make this search the default behaviour, unless a value has been provided manually. This would involve:
   • Changing kFALSE to kTRUE on line 964 of LauIsobarDynamics.hh
   • Removing the autoSetASqMaxValue function
   • Modifying the setASqMaxValue function to additionally set aSqMaxAuto_ = kFALSE;
3. The LauFitter::destroyFitter function should probably also restore the default values of fitterType_, fitterVerbosity_, and fitterMaxPars_

Does this sound reasonable to you?

1. Yes, increasing the safety factor to 10% is sensible.

So I gather from looking at the code that the fit procedure uses certain masses (m0) and cosTheta values to provide an initial guess, then the ASq likelihood is maximised by varying m13Sq and m23Sq as parameters?

Could we instead use a square Dalitz plot with binned m'-theta' values to initialise, test and fit ASq? That way we can systematically cover important areas of the Dalitz plot.

2. Yes, making the search the default behaviour is a good idea, although allow the user to specify it if required.

3. Reverting to default values sounds OK when deleting the fitter function.

Hi John,

So I gather from looking at the code that the fit procedure uses certain masses (m0) and cosTheta values to provide an initial guess, then the ASq likelihood is maximised by varying m13Sq and m23Sq as parameters?

Could we instead use a square Dalitz plot with binned m'-theta' values to initialise, test and fit ASq? That way we can systematically cover important areas of the Dalitz plot.

The values of m0 are taken by looping through the components in the model for which we're trying to find the maximum. So I think this is probably better than using a fixed grid pattern (in a similar way to how we tweak the integration scheme depending on the contents of the model). It seems that expanding the nested loop so that it runs over mass : { m0 - Gamma0, m0, m0 + Gamma0 } and cosHel : { -1, 0, +1 } works better than just using m0. We'll upload those changes (and the others mentioned previously) shortly.