Jet Resolution Improvement

Techniques

Ariel Schwartzman, Chris Tully

Princeton University

D0

In collaboration with Anna Goussiou, Guennadi Obrant, and Prolay Mal.

TeV4LHC Meeting, Fermilab, 21-Nov-2005

Outline

- Jet reconstruction at high luminosity:

- Algorithm for calorimeter jet and vertex association.

- Improving jet energy resolution using tracks:

- TrackCalJet algorithm overview.

- Studies in simulated events:

- Zbb mass resolution.

- Studies in photon + jet data events:

- jet energy resolution and response.

Jet Multiplicity at High Luminosity

At DZero, the beam spot has a width of 25cm in Z.

Average jet multiplicity increases with number of primary vertices.

Jet kinematics is distorted at low pT (extra low-pT jets)

this effect will be much more important at the LHC.

W+ ³ 1 jet data events

z

vtx 2

vtx 1

CPF(vtx1) = 0

CPF(vtx2) = 1

CPF(vtx1) = f

CPF(vtx2) = 1-f

Goal: Associate Jets to Vertices.

Jet Multiplicity vs. Number of Vertices

The dependence of jet multiplicity on the number of interactions is significantly reduced for jets with more than 75% of its energy coming from the hard scatter vertex.

Jet-Vertex algorithm allows to identify (remove) soft jets arising from min-bias interactions.

Improving Jet Energy Resolution Using Tracks (I)

Cal-Jet energy resolution has three (four) main contributions:

- Electronics noise term ~1/E

- Stochastic response ~1/sqrt(E)

- Constant term

non-uniformity of calibrations

non-linearity, magnetic field effects

- Absolute energy scale (denominator in sigma/E)

Idea:	- Reconstruct calorimeter-based jets (R=0.5)

- Use track momentum measurements to set an accurate	scale for hadron response for each hadron in the jet.

The Tevatron is an excellent opportunity to understand and optimize this technique on real collider data.

Addressed by

Tracks

CMS JetPlusTrack Algorithm

Improving Jet Energy Resolution Using Tracks (II)

Normally Jet Energy Scale (k) does not

affect resolution.

TrackCalJets:

Do not rescale the raw calorimeter energy measurement.

Replace contributions to k from charged hadrons by the tracker	momentum scale:

Cal-Jets:

Small contribution

cal-JES still needed

Measured with Tracks

k does not take into account the non-linear

response of individual particles in jets.

Why Does it Work?

Cal-cluster

Track

Normal JES: Average non-linear response assuming particle

multiplicity and momentum spectrum

TrackCalJets: Sum individual contributions using the tracks

of each jet.

Normal JES

TrackCalJets

Propagate tracks to the calorimeter surface.

dca(xy) < 0.5cm, dca(z) < 1.0 cm.

Classify tracks:

DR(vtx)<0.5, DR(cal)<0.5 : IN jet

DR(vtx)<0.5, DR(cal)>0.5 : Out-of-cone

For each IN-jet track:

For each Out-of-cone track:

where:

is the single pion average calorimeter response.

TrackCalJet Algorithm Overview

In-Jet

Out-Jet

CMS JetPlusTrack Algorithm

Single Pion Response in the Simulation

Single particle response measured in single pion Monte Carlo.

Currently studying minimum-bias data, and developing a dedicated

trigger for high energy pions.

Response is significantly improved with the use of tracks.

12% Improvement on mass resolution.

Zbb Mass Resolution in the Simulation

17% Improvement on Zbb mass resolution by using a larger cone size for tracks: Important for Higgs searches, in 2-jet events.

TrackCalJet Algorithm Optimization:

Track Cone Size

Track

Cone = 0.7

Track

Cone = 0.9

20% Improvement on Zbb mass resolution by using all tracks in the jet: Need to be re-optimized in the data.

TrackCalJet Algorithm Optimization:

Track Quality: SMT/CFT hits

0 SMT

12 CFT

0 SMT

0 CFT

Jet Resolution in photon + jet Data

10-20% Resolution improvement in data at 40 GeV.

Higher improvement at lower jet energies.

Jet resolution improves by using larger track-cone sizes.

Cal-cone size = 0.5

TrackCalJet Energy Scale: Response (I)

Response measured in

photon +jet data.

Tracking acceptance: |eta|<1.5

Number of tracks

TrackCalJet Response is derived as a function of jet pT and track multiplicity.

Jets which have large charged content get larger track correction.

Jet resolution also improves

as a function of number of tracks.

TrackCalJet Energy Scale: Response (II)

Tracks close to the boundary of the jet, will not deposit all their energy inside the calorimeter cone.

Current TrackCalJet version assumes all pion energy is contained in the jet, i.e. it is over-subtracting energy:

Next version of the algorithm will consider

this effect introducing a showering correction:

TrackCalJet Future Improvements (I)

Showering Correction

Expect improvement on

both response and resolution.

Summary and Conclusions

Jet reconstruction at high luminosity:

- Additional min-bias interactions produce extra soft jets.

- Jet-Vertex association algorithm allows to discriminate between jets	arising from the hard scatter vertex and additional minimum bias	interactions: Improves jet momentum.

Improving jet energy resolution using tracks:

- CMS technique for combining tracks and vertices has been	implemented at DZero.

- 10-20% jet resolution improvement in photon+jet data and MC.

- Increasing the track-cone size further improves the jet resolution.

- Currently finalizing jet energy scale. Expect to use this algorithm for

DZero 2006 physics results.

Backup slides

Jet-Vertex Identification

z

vtx 2

vtx 1

Goal: Associate Cal-Jets to Vertices.

Algorithm:

- Select tracks matched to vertices and jets:

∆Z(trkj , vtxi) < 1.0cm, ∆R(trkj , jeti) < 0.5

- For each jet, calculate the fraction of track energy associated to each vertex:

associate jet j to the vertex i for which CPF(jetj , vtxi) is maximum.

CPF is the charged particle energy fraction of jet j from vertex i.

CPF(vtx1) = 0

CPF(vtx2) = 1

CPF(vtx1) = f

CPF(vtx2) = 1-f

Third leading jet

CPF in W+3 jet Events

The 3rd leading jet is more likely to be associated to a min-bias interaction.

Min-bias

jets

No-track

information

Min-bias

contribution

Third leading jet

2005-01-29T17:19:08