1. Bernard Andrieu (LPNHE,Paris)
Noise (in) jets study
Bernard Andrieu (LPNHE,Paris)
JetMET meeting 02/27/2003

2. Goal Separate “good” from “fake” jets
Definition: “fake” = made out of noise (+ possible minimum bias energy)
“good” = not “fake” (still it can include some noise!)
What do “good” (and “fake”) jets look like?
How to select “good” jets (before knowing what they look like)?
Use dijet events
Data: Run , , (K events), p
No trigger requirement. Processed with top_analyse.
Cone 0.5 jets, standard ID cuts (without CHF cut)
 < EMF < 0.95
 n90 > 1
 HotF < 10
Monte-Carlo: ctf_p1308_qcd_pt40_sig2.5_tmb

3. How to select good jets? (I)
Trigger validation: L1set
a Could bias selection
(CH not included in L1 readout)
Other selection variables:
Jet-track matching: dR(jet,track)
a Could also bias selection
(noise added to a good jet might change its direction)
Jet-Jet matching in the transverse plane: DeltaPhi (=0 for back-to-back jets)
a Same remark as for track-jet matching
Missing Et: Ptmiss/sqrt(ET)
a Could also suppress noisy (but still) “good” jets

4. How to select good jets? (II)
(without introducing selection biases)
Solution (?):
- Select events with only two jets (even before quality cuts)
Ask for leading jet to be
then look at second jet
validated by L1 energy,
(L1set/PT <0.3)
f90
- Cross-checks between variables
l1set/PT
(see effects on one variable when cutting on another independent one)
- Compare with Monte-Carlo

5. How to select good jets? (III)
minimal selection (events with at least two good jets, take the most back-to-back if N>2)
exactly two jets (no 3rd jet, even not passing the quality cuts)
leading jet validated by L1 energy (l1set/PT>0.3)

6. How to select good jets? (IV)
1.5< L1set < 2.2
L1set=0.
0.5< L1set < 1.5
L1set cut (second jet):
Effect on other variables:
dR(cal-track) DeltaPhi

7. Final good jets selection
Exactly two jets (no 3rd jet, even not passing the quality cuts)
Leading jet validated by L1 energy (L1set/PT>0.3)
Minimal requirement on L1 energy for second jet (L1set/PT>0.)
40 < S PTjet < 60
60 < S PTjet < 80
80 < S PTjet < 100
DeltaPhi MC
Data
100 < S PTjet < 140
140 < S PTjet < 200
200 < S PTjet < 300
Reasonable agreement DATA/MC

8. f90 for good jets (data vs MC)
f90 depends mainly on the number of merges
otherwise data and MC similar

9. emf & chf for good jets: (data vs MC)
0 merge
1 merge
> 1 merge
Discrepancy DATA/MC for high emf (0 merge) and high chf

10. f90 correlations for good jets (data)
f90 vs log10(E)
f90 vs chf
merge=0
merge=1
merge>1
f90 depends on E for merge<=1, doesn’t depend on chf (at a fixed merge)

11. f90 correlations for good jets (MC)
f90 vs log10(E)
f90 vs chf
merge=0
merge=1
merge>1
Lack of statistics at low E, similar correlations as in data

12. How to select fake jets?
Exactly 2 jets & at least 1 jet not validated by L1 energy

13. Distributions for fake jets
Leading jet
Second jet
merge
eta
f90
More merges than in data, but similar f90 at fixed merge

14. f90 correlations for fake jets
f90 vs log10(E)
f90 vs chf
Leading jet
Second jet
Lack of statistics, similar correlations as in data (for merge> 1)

15. Summary “good” jets may have high values of f90
 Confirmation of effect seen by Vishnu on Monte-Carlo:
“good” jets may have high values of f90
 Pure fake jets don’t seem to be a problem:
How to reduce the noise for “good” jets?
 Several (complementary?) approaches
1) get rid of cells with high occupancy (run by run) Robert
2) get rid of individual isolated noise cells (event by event)
T42 algorithm (Jean-Roch)
3) get rid of noisy preclusters in CH to avoid merges in excess
modify seeding algorithm to raise threshold in CH (Emmanuel)
4) subtract mean noise contribution to jets, depending on eta, number of merges,
energy in CH?
 To do: estimate efficiency and purity of jets with present quality cuts, compare with MC at lower PT, improve quality cuts, re-run jet algorithms with modified seeding on physics sample (e.g. W+jet(s)), subtract noise, etc…