Calibrating Jets with weighted TopoCluster ATLAS Hadronic Calibration Workshop Munich, 3-5 May 2006 Ambreesh Gupta, University of Chicago

JetCalib JetCalib package is a general. 1. Monitor performance of calibration schemes. (See talk in Dec software week) 2. Derive calibration weights by using function minimization technique. Details on how to use can be found at - https://uimon.cern.ch/twiki/bin/view/Atlas/JetCalib JetFit is the top level algorithm Three examples of function building and minimization 1. FitSample Layer info kept in a vector untill end of 2. FitSampleIn2EnergyBin loop and then function built. General. 3. FitCellDenH1Style A matrix is updated every jet. Fast. Interface to Minimizer - SEAL interface to Minuit, (Gemini for NAGlib a CERN)

Sampling Weights Each jet divided in two layers EM(LAr) and HAD(Tile, HEC, FCAL) Fit is done in -- 50 eta bin -- 2 energy bin -- for each energy bin a weight is function of energy a + b log(E) Un-calibrated Calibrated

Fit for cell energy density An example of deriving weights similar to H1 style weights. one eta bin 0.0 - 4.4, gap and crack regions excluded fit is performed to binned cell energy density in different calorimeter. functional form - a +b*i + c*i2 + d*i3 , where “i” is bin number. 43 parameters in the fit. w[0] = 1.263, w[1] = 1, w[2] = 1.051, w[3] = 1.022 wtEMB1[16] = 1.52, 1.49, 1.45, 1.42, 1.38, 1.35, 1.31, 1.27, 1.23, 1.19, 1.16, 1.12, 1.09, 1.06, 1.04, 1.02 wtEMB2[16] = 1.72, 1.68, 1.63, 1.57, 1.52, 1.46, 1.40, 1.34, 1.29, 1.23, 1.18, 1.13, 1.09, 1.05, 1.025, 1 wtEME1[16] = 1.93, 1.92, 1.90, 1.87, 1.82, 1.75, 1.65, 1.54, 1.40, 1.23, 1.04, 0.80, 0.54, 0.24, -0.09, -0.4 wtEME2[16] = 1.28, 1.28, 1.28, 1.28, 1.27, 1.26, 1.25, 1.24, 1.22, 1.20, 1.18, 1.15, 1.11, 1.08, 1.03, 0.98 wtTile1[16] = 1.78, 1.70, 1.63, 1.55, 1.47, 1.40, 1.34, 1.28, 1.23, 1.20, 1.18, 1.17, 1.18, 1.22, 1.28, 1.36 wtTile2[16] = 1.81, 1.68, 1.55, 1.43, 1.32, 1.22, 1.14, 1.07, 1.02, 0.98, 0.98, 0.99, 1.03, 1.10, 1.20, 1.34 wtHec1[16] = 1.08, 1.14, 1.20, 1.25, 1.29, 1.33, 1.35, 1.36, 1.36, 1.34, 1.29, 1.23, 1.15, 1.03, 0.89, 0.73 wtHec2[16] = 2.15, 2.06, 1.94, 1.82, 1.70, 1.59, 1.48, 1.38, 1.30, 1.24, 1.20, 1.19, 1.21, 1.27, 1.38, 1.52 wtFcal1[16] = 1.75, 1.74, 1.74, 1.73, 1.71, 1.69, 1.66, 1.62, 1.58, 1.54, 1.48, 1.43, 1.36, 1.29, 1.22, 1.14 wtFcal2[16] = 1.09, 1.18, 1.25, 1.31, 1.38, 1.48, 1.43, 1.45, 1.42, 1.46, 1.48, 1.42, 1.38, 1.34, 1.28, 1.21 Not optimized. Some negative weights!

Calibrating Jets with weighted TopoCluster Using release 11.0.41 Cone jets (0.7) made CaloTopoCluster (4,2,0) CaloTopoCluster are weighted using, -- #include ("CaloClusterCorrection/CaloTopoLocalCalib_jobOptions.py") To derive sampling layer weights of Jets, weighted CaloCell energy was used. Preliminary results - more of questions than definite answers on how to use weighted CaloTopoCluster for jets. Also tried testing stability of weights -- by changing noise sigma by 50% (?) -- But running in some software problem when reading in modified CaloCell.

TopoCluster TopoCluster Jets from Weighted Topo Jets from Weighted Topo Applied with extra eta and sampling dependent weight

Jets from Weighted Topo Applied with extra eta and sampling dependent weight Jets from Weighted Topo

Systematics on Jets Input jet spectrum top Di-jet Di-jet Jet Et (GeV) About 6% shift due when using weights from di-jet events in top event at low energies. The difference can be accounted for due to input jet spectrum and underlying event differences. Various other sources of systematics to consider How do the uncertainties in modeling of the event affect the ability to tag EM clusters from hadronic cluster? top