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

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 169183, 169222, 169262 (K events), p13.05.00 No trigger requirement. Processed with top_analyse. Cone 0.5 jets, standard ID cuts (without CHF cut)  0.05 < EMF < 0.95  n90 > 1  HotF < 10 Monte-Carlo: ctf_p1308_qcd_pt40_sig2.5_tmb

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

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

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)

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

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

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

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

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)

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

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

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

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)

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…