1. Types of Data SimTracks: generated particles SimHits: energy depositions in a detector volume Digis: Single-channel pieces of the detector’s raw binary output RecHits: Reconstructed energy and position for a single detector elements DST Objects: Higher level things, like jets & tracks

2. Software Components u Generator (such as PYTHIA) è Creates SimTracks è Stores in PAW ntuples in a standard format (HEPEVT) u Simulation (OSCAR) è Swims the tracks through the detector, leaving energy deposits (SimHits) u Fast simulation (FAMOS) è Uses parametrized resolutions to smear the SimTracks, and make higher level (DST) objects u Reconstruction (ORCA) è Simulates detector signals (Digis) from the SimHits è Reconstructs (RecHits) & higher level (DST) Objects è Can run on these objects to make ROOT trees u Visualization (IGUANA)

3. Starting out with CMS software Starting out with CMS software First place to start is the ROOT file  Get account on Fermilab analysis machines  Pick your favorite dataset  Single-particle sets are fine for now.  Start with a “Digi” or “DST” dataset, not “SimHits”  Follow the instructions in Eric’s talk to run ExRootAnalysis  Make ROOT plots of everything that might interest you  Pick out the calorimetry clusters in some eta/phi radius around certain type of generated particle  See what kind of energy resolution you’re getting  Can you see brem effects in electrons?  How is energy shared between ECAL & HCAL?  How does it depend on eta?  How many generated tracks are there around this particle?  Do they come from pileup? Underlying event?  How many reconstructed tracks?

4. Learning about reconstruction algorithms Learning about reconstruction algorithms  Try to figure out which algorithms make your favorite reconstructed object  “grep”ping through the codebase is your easiest option  DON’T try to follow the logic of how ORCA actually calls that algorithm  Look through CMS notes for reconstruction algorithms  (don’t expect much documentation for the code)  See what parameters can be varied in the.orcarc  Put some “cout” statements in, scram build, and run ExRootAnalysis again  Run from a “Digi” dataset this time, to make sure everything gets rebuilt. The “DST” dataset will already have the objects, so they won’t get rebuilt.

5. New Framework u Timescale is spring/summer 2006, for cosmics tests u More rigidly-structured than ORCA è Data is “pushed”, not “pulled” u Stored in ROOT format, so you can plot the data directly from ROOT