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

2. 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 -
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)

3. 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

4. Fit for cell energy density
An example of deriving weights similar to H1 style weights.
one eta bin , 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!

5. Calibrating Jets with weighted TopoCluster
Using release
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.

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

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

8. 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?
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