# Presentation: 356169

## Jet ID experience at DØ

- Amnon HarelUniversity of Rochester

***Jet ID:***
-  Choose energy to be clustered into jets
-  Choose clustering algorithms and parameters
-  Choose sets of selection criteria
- Prepare & optimize jet ID tools
- Choose a basic set of cuts
- Fancier optional cuts (e.g. jet-vertex matching)
-  Measure reconstruction and selection efficiencies
- For this talk I’ll concentrate on choosing the basic set of selection criteria (3b)

## Jet selection timeline

- “practical” choices, driven by specifichardware problems

**First comprehensive**** “optimization” study**

- This data published with these cuts, no further work expected.

- First (preliminary) results with jets

- Development of jet ID tools

## Geometry

**The No EM Gap**
**Lots of CH with less calorimetry than normal before it...**

- CH1

- CH1

- CH3

- FH

- FH

- FH

- EM

- EM

- CH2

- CH1

**Beam collision area**

## How we choose jet criteria

- Choosing samples enriched and depleted in unphysical jets
- We used the independent level 1 trigger readout to verify which selections really enhance jets with precision-electronics noise
- We assumed those samples would also be enriched in other unphysical jets
-  Identify and study unphysical jets
- Used brand new, and as we later found out, very good data quality criteria  identified only one type of unphysical jets in our samples
-  Study selection efficiencies for physical jets
- We identified unmotivated inefficiencies
-  “Optimize” the overall selection
-  Arbitrary decision: is a jet with 30% noise a “bad” jet? What about 70%?
-  Minimize bias, e.g. nice to have a similar efficiency for quark and gluon jets
-  Purity will vary greatly from analysis to analysis
-  Separate into η and pT regions when selecting cuts
-  We aimed at 98-99% efficiency and removal of all identifiable noise
- Caveat: JES procedure required the cut EMF<0.95
- Note: no MC events were used

## The samples: good jets

- Physical-Jet enriched sample: tag & probe di-jet
***Basic cuts***
- A jet trigger (required for all samples)
- |P.V.Z.| < 30cm (required for all samples)
**Dijet, tag jet passes jet ID**
**2**** ****GeV track match**** (for |****η****|<2.0)**** **
**ΔΦ**** > 3.0**

- These jets just “have” to contain physical energy – consider them as signal

***Fancier details***
- MET < 20 GeV
- |METT| < 8 GeV
- METT for the dijets is the (size of the) MET component perpendicular to the leading jet.
- pT balance (ΔpT/<pT>  < 0.5)

## The samples: too many jets

- Physical-Jet depleted sample
**6 jets with |η|<2.5**** (implied: p****T**** > ~12**** ****GeV)**
**choose a jet without a track match**
**p****T**** > 0.5 GeV**
**Δ****R < 0.5**
**Loose track quality **
**Fancy detail: ****MET > 20**** ****GeV ****(when |η|<1.5) **

- Without jet ID, this strongly enhances the sample in calorimetry noise (and top pairs)

- And this selects the suspicious jets in the sample

- Noise enhancement

- No enhancement in end caps

## The samples: 3rd jet

- Physical-Jet depleted sample
**2 great, balanced jets**
**|η|<2.5**
**pass jet ID**
**2 GeV track match (for |η|<2.0) **
**ΔΦ > 3.0**
**A third suspect jet**
**At least ΔΦ > 1.2 with both of them**
**Has no 0.5**** ****GeV track match**
**Fancy detail: ****METT < -5**** ****GeV ****(when |η|<0.8) **

- Noise enhancement

**Tag jet**

**Tag jet**

**3****rd**** jet**

- In the CC it can greatly enhance the sample in L1 rejected jets, though it
- throws out many interesting suspicious events.
- In other regions the enhancement, if it exists, is not worth the statistics.

- METT for the 3rd jets is the MET component in their direction. In a perfectly clean event with a perfectly fake 3rd jet:

## The samples: kinematics

- The jet kinematics differ greatly between the samples!
-  cuts optimized in η and pT regions, which largely overcomes this problem
-  also played with reweighting the good jet sample to match the bad jet kinematics

**Raw p****T**** [GeV] **

**η**** **

**dijets**
**too many jets**
**3****rd**** Jets**

- All pT and E are rawJES is from ~2 at low pT                to ~1.2 at high pT

- BTW: as the NS asymmetry hints, this study was done before the final calorimetry calibration was in

- Inter cryostat region is noisier, with inferior energy resolution

## Noise studies

- Identified a type of fake jets: in busy events coarse hadronic noise is gathered into wide hard jets. But this only happens in busy events where a seed is available (jet algorithm has a veto against CH seeds). This conclusion is supported by the pT dependent CHF, ηdet, φ-width, P.V.Z., n90 and MET distributions. Since physical ICR jets with a high
- CHF are narrow, this can be used to discriminate against this background.

**dijets**
**too many jets**
**3****rd**** Jets**

**1.5<|****η****|<2.5**

**0.8<|****η****|<1.5**

**CHF**

**CHF**

**n90**

- A measure of jet width

** ****Looked suspicious**
** ****Proven to be noise**
** ****Reject with CHF<0.6 **

**CHF<0.4 || (CHF<0.6 && 8.5<η****det****<12.5 && n90<20)**

**More work required at low p****T**

**& 20<p****T****<40**

## Selection efficiency

- We set cuts on minimal EM fraction and L1 confirmation to achieve a selection efficiency (in the “Good jets” sample) of 99.7-99.9% per cut.

**0.8<|****η****|<1.5****, L1Conf, .4>chf, p****T****>40**

**0.8<|****η****|<1.5****, L1Conf, .4>chf, p****T****>40, ****emf<.05**

- Requiring EMF > 5% is too inefficient in the no EM gap, pT dependence checked, and is negligible (high pTnarrow jetsmore inefficiency but in same place)
- There is separation between the samples in EMF even for narrow jets in the gap region, but it is too weak to be useful.
- Furthermore, the “bad” jets in these regions look like single particles, rather than unphysical energy

**No minimal EMF**
**in this region**

**Looser EMF cut ****in this region**

**|****η****det****|**** **

**η**** width**

**η**** width**

## Closing comments

- Jet selection criteria are not a good thing, as they imply that perfectly physical jets are ignored. Typical rate o(1%).  What about MET?
- Most new physics contain a dark matter candidate  MET signatures
- The fake MET caused by ignoring physical jets can be simulated only if fake rates are well simulated  use data as much as possibleReached o(1%) accuracy with 1fb-1 of data
- Vetoing any event with a “bad” jet?
- sometimes a good solution
- hard to evaluate the efficiency of the veto without a simulation of the noise...

**Jet production**

**New physics?**

## Conclusions

-  Mostly data driven choice of basic jet ID criteria
- The data shaped the studies
-  Tracking helps
-  It really helps to have a reliable ID for at least one kind of noise
-  Correlations with MET can help identify unphysical jets (at high enough pT)
-  Details of noise characteristics and detector geometry are relevant
-  With the tools in place, quick turn around is possible
- Both ID tools (variables) and optimization macros
- We had a two week time window from the time the (partially calibrated) data was available to full collaboration approval & official implementation.

## Back Up Slides

## ?

- b