Physics Performance of the CMS Reconstruction Software with the First LHC Collisions David Lange Lawrence Livermore National Laboratory Representing the CMS collaboration CHEP 2010 David Lange, LLNL

d CHEP 2010 David Lange, LLNL

Reconstruction process turns detector hits into analysis level objects Local Detector Reconstruction Physics object reconstruction High-level reconstruction Tracker hits HCAL clusters ECAL clusters Muon segments Tracks Photons Muons Electrons Jets Missing energy b tagging Particle Flow Tau Vertexing We have the ability to redo high-level reconstruction components at the analysis stage. This provides flexibility to include latest developments CHEP 2010 David Lange, LLNL

Results of prompt reconstruction is already of sufficient quality for analysis High-level trigger Real-time local and regional reconstruction Express reconstruction Availability: ~60 minutes Usage: Calibrations, data quality Prompt reconstruction Availability: 48 hours Re-reconstruction Usage: Make coherent data sets (e.g., consistent software release, calibrations) Monte Carlo (Re)Reconstruct Geant4 samples to match reconstruction used in data Already achieved good operational stability. Working to incorporate lessons learned from initial data analysis into official reconstruction algorithms CHEP 2010 David Lange, LLNL

CMS production scheme has led to reliable offline operations Code development cycle: Nightly and ~weekly validations 6-8 week dev. cycle Production using small subset of data/MC Conditions updates Production release Pre- Production Validation group signoff? Code bug fixes No Validation procedure is transitioning to be data driven in both the code development and pre-production stage At this stage, the validation sign-off is largely performed by hand Yes Production start CHEP 2010 David Lange, LLNL

Lightning tour of CMS reconstruction methods and results Validation procedures enabled us to derive most ICHEP results from prompt reco Results are primarily from ICHEP2010 CHEP 2010 David Lange, LLNL

CMS tracking in a nutshell CHEP 2010 David Lange, LLNL

CMS tracking in a nutshell CHEP 2010 David Lange, LLNL

CMS tracking in a nutshell CHEP 2010 David Lange, LLNL

We achieved excellent tracking performance Energy scale: KS mass to 0.3 MeV over kinematic range Material model: ~10% agreement with MC model) Track parameters / impact parameter agree with MC 99+% tracking efficiency measured on J/ymm data CHEP 2010 David Lange, LLNL

Muon reconstruction Tight muon selection requires combined fit of tracker and muon system hits and hits in at least 2 muon stations Soft muon selects muons down to 0.5 GeV Tracker matched to 1+ CSC or DT segs. CHEP 2010 David Lange, LLNL

Muon ID performance: 90+% efficiency above 7 GeV Tag/Probe efficiency measurement Mis-ID measurement CHEP 2010 David Lange, LLNL

Di-muons in CMS CHEP 2010 David Lange, LLNL

Photon candidates built from superclusters. Purity increases with pT. Observe p0 and h resonances in agreement with MC expectations. Correct energy scale (O(1%)) CHEP 2010 David Lange, LLNL

Electron Identification Utilize two complementary approaches Start from ECAL superclusters, then look for hits in tracker Start from tracks, then look for ECAL hits Brem. energy-loss model to fit electron track (GSF) Preselect candidates based on matching criteria. Apply further selections at analysis level based on efficiency and fake requirements (fake rate measured to be 10-3-10-2 depending on working point and pT) CHEP 2010 David Lange, LLNL

Particle Flow reconstruction in CMS Identify individual particles (tracks and clusters). Use to build jets, electrons, identify taus from their decay products, tag b jets, etc… Example application: J/yee CHEP 2010 David Lange, LLNL

Jet reconstruction Single particle response Jet pT resolution Data vs MC agreement at about 10% level or better already PF approach improves resolution significantly over calorimetric method CHEP 2010 David Lange, LLNL

Missing ET : Calorimeter noise cleaning algorithms bring MET performance to MC expectations Data/MC agreement in MET distribution is seen over several orders of magnitude. Particle-flow based MET techniques show improved performance over calorimetry based techniques Both event and cluster level calorimeter cleaning algorithms developed and applied for ICHEP results CHEP 2010 David Lange, LLNL

B-tagging discriminator algorithms: Commissioned from the very beginning Jet Probability Algorithm tags jets according to the probability of all the tracks in the jet to originate from the primary vertex, given their IP significances Track Counting Algorithm tags jets containing N tracks with Impact Parameter (IP) significance exceeding S High Purity configuration: N=3 SSV Algorithm tags jets according to the 3D flight distance significance of the reconstructed secondary vertex High Purity configuration: Vertices with 3 or more tracks CHEP 2010 David Lange, LLNL

CMS is in the midst of a successful first run Thanks to careful preparation prior to the first data: the reconstructed data shows an exceptional agreement with expectations for all the high-level physics objects The rapid increase in luminosity will bring further improved calibrations and algorithms Zmm CHEP 2010 David Lange, LLNL