sample_luminosity function

This utility will calculate the luminosity for a given trigger and data sample and return a bad lbns list for analysis

Syntax is:

sample_luminosity <listoffiles> <parentagepath> <triggername>

It expects any good runs to be in goodRunsList, bad runs in badRunsList and bad lbns be in badLBNList

Output is 3 files

<listoffiles>_<trigger>.lum summary of luminosity information

recorded_<listoffiles>_<trigger>.badlbns list of bad lbns, including the input bad runs and lbns for recorded data

reconstructed_<listoffiles>_<trigger>.badlbns list of bad lbns, including the input bad runs and lbns for reconstructed data (includes checks on processing)

HOW TO USE THIS TO GET A NORMALIZED CROSS SECTION

• Get your parentage for your common sample:read D0News by Marco Verzocchi

  setup D0RunII p14.06.00
  /home/mverzocc/scripts/genLBNtables.py -skim <skimname>

  This will create a subdirectory ./<skimname> which is your parentagepath

• Create a list of files for your sample -> listname

• Create a list of bad runs for your sample in badRunsList
  Or a list of good runs in goodRunsList

• Create a list of bad lbns for your sample in badLBNList

• Run the new program

/home/schellma/sample_luminosity <listname> <parentagepath> <triggername>

It will produce a luminosity summary and lists of bad lbns for your sample – this is the OR of bad lbns from your bad runs bad lbns and those known to be bad in the normalization. Use this list in your analysis!

• In your analysis code throw out the bad lbns from all sources

Use the class lm_access/BadLBNList
In p14-br (not released) and t04.00.00

#include “lm_access/BadLBNList.hpp”

// initialize and read in the badLBNList from sample_luminosity
lm_access::BadLBNList* pbadlbnlist = new lm_access::BadLBNList();
pbadlbnlist->loadBadLBNs("./badLBNList");

// in your event loop – cut out the bad lbns
if(pbadlbnlist->isBadLBN(lbn)) continue;

// at the end
delete pbadlbnlist;