Checked pT conservation - good within 0.5%
Can make non-approx mass plots - use invariant mass as the mass (momb+mombbar).mCalc()
eta,phi,pT map for the two processes
put code in git for now, might include in appendix
Have a skeleton that can plot the classifier output with fraction of right/wrong
How to judge the efficiency of ML- look at ROC curve
Meet next time 3pm on 13th March
Checked pT conservation - good within 0.5% Can make non-approx mass plots - use invariant mass as the mass (momb+mombbar).mCalc() eta,phi,pT map for the two processes put code in git for now, might include in appendix Have a skeleton that can plot the classifier output with fraction of right/wrong How to judge the efficiency of ML- look at ROC curve Meet next time 3pm on 13th March