Using TMVA with many backgrounds


I’m wondering how I should use TMVA with more than one background, especially when cross-sections for them differ significantly (few orders of magnitude). To complicate more big backgrounds are not a problem (I can eliminate them with few simple cuts) but those with small cross-sections are very problematic. Should I use weights to scale their relative proportions to correct values or just leave them as they are. I would greatly appreciate some explanation on this topic.


I’m not a NN expert, but…

If you are using a 1 node output tree (or whatever)

  • Make the simple event selection you want.
  • Make the background sample you are using for training have the same background that you expect.

You can also train a net that has more than one output node (e.g., first node is signal versus background 1, second node is signal versus background 2, etc.).

Hope this helps,