Introduction
From a first look at the AcdV2 cuts applied to Pass8 data, it appears that the selection is not optimized. The goal here is to create a framework for designing an AcdV2 selection. Since it is likely that some of the Pass8 foundation will change along the way, the emphasis here is to develop tools and a methodology for creating this type of analysis.
Background
- At Eric's suggestion, I looked into some of the Markus' work on Pass7 event classes. Specifically, the plot on slide 3 seem very useful, and I have started to replicate them with ACD variables.
- To do an energy dependent selection, we must have an energy measure. Carmelo has done some work to create simple energy selection for Pass8. Implementing this energy selection should be sufficient for the first step.
Code Base
I've started to collect some of the code I've been developing in CVS:
users/kadrlica/eventSelect/python/
However, the code is rough and should be used at your own risk!
Event Samples
Monte Carlo
The basic event samples I've been using come from the calibration data sets that Johan has produced.
Data
Marco Tinivella should be able to help with this and has some code that depends on Luca's MeritUtils:
/users/lbaldini/MeritUtils/