I have a question/remark on the procedure about making RooDataSets given a RDataFrame snapshot TTree.
We observed in our analysis some strange behaviour, which we figured out being related to how Snapshots are done from RDataFrame. As we understood, the Snapshot in MT mode enables shuffling of entries order in the final TTree. If we use the shuffled/unshuffled TTree and we create a DataSet to fit later for it, we do observe different final results.
The fit becomes completely deterministic when exactly the same TTree index order is used.
Therefore the question:
Assuming a fitting routine takes an Input TTree, apply a cut to it and make a DataSet out of it, is it a known behaviour that the order in which a RooDataSet get filled can modify the final results?
I do expect this to happen because of the
Strategy used :
Where the strategies chunks the data in n-equal slots, but if one shuffles the dataset entries order, the chunks division becomes not deterministic.
I admittely didn’t know that the fit results can depend on the order in which a dataset is created, but i wonder if there are any more robust and deterministic approach to use here to always get the same result even with a shuffled dataset order filling scheme.
Thanks in advance ,