Best Fit ? Estimated distance to minimum


I’m trying to (ML) fit my data with a crystal ball function. Every time I change the initial value of one of the parameters I get a completely different fit (with different number of signal events).
For all these fits I get the same minimized FCN value but with a different estimated distance to the minimum.
How can I know which one of my fits is better? Can I rely on the estimated distance to the minimum?
The fit with the smallest distance would be the best?



It is really strange that you get different solutions for the same FCN value. Are you sure that the implementation of your FCN is correct ?
I would try to scan it as function of one or two parameters



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