Calibration of ATLAS JetsCalibration of ATLAS Jets

Robert Kehoe

1st North American ATLAS Physics WorkshopDecember 20, 2004

Southern Methodist UniversityDepartment of Physics

Jets and Physics

Tevatron Jet Energy Scale

ATLAS Considerations

Where am I Going with This?

Jet ConsiderationsJet Considerations

• a jet is a composite object• complex underlying physics

• associated soft interactions at hadron colliders• connected to rest of event - color• fragmentation• initial and final state gluon radiation• different types: light quarks, gluons, b/c/

• complex detector properties• non-linearities and dead-regions in response• particle shower widths• digital filtering of signals

• algorithms often have complex behavior• out-of-cone• underlying event• unreconstructed energy• merge-split

• two levels• particle: primarily correct for detector/accelerator effects• parton: correct to a more theoretical physics level obscured by algorithm

Jet PhysicsJet Physics

• QCD• eg. cone and kT algorithms used• testing perturbative QCD calculations, PDFs• looking for evidence of quark compositeness• important concerns

• appropriateness of algorithm to perturbative QCD calculations• energy scale over widest Et range (to 2-3 TeV @ LHC)• well-understood forward scale also to very high Et

• New phenomena searches• energy scale over very wide Et range• interplay with Etmiss important

• Higgs: energy flow to improve E resolution• Top

• wide kinematic range for primarily central jets• crowded events: smaller cone sizes?• parton level corrections

• not needed for cross section?• necessary for top mass

Tevatron CalorimetryTevatron Calorimetry

• D0 calorimeters (||<5.2):• LAr sampling, U/steel absorber• fine granularity• nearly compensating• pulsers to calibrate electronics, SCA non-

linearities• jet confirmation via Level 1 readout comparison• Monte Carlo carries over Run 1 test-beam for Run

2

• CDF calorimeters (||<3.6):• Scintillating tile, lead/iron absorber• coarse granularity• Non-compensating• test beam calibration carried over via 137Cs

sources• light pulsers used for time-dependence in

PlugWall Had

CentralNew Plug

End Calorimeters

Fine hadronic

Electromagnetic

Coarse hadronic

Central Calorimeter

PEM

absolute EM scalein situ measurement of Z’s, et al.

Structure of Jet Energy ScaleStructure of Jet Energy Scale

1. relative correction vs. : frel

2. Multiple pp Interactions : UEM3. correct to particle level jets: fabs 4. Underlying Event: UE5. Correct back to parton: OC

• primary features:• use Monte Carlo to model

single particle response• anchor all measurements to

Central• main terms from Pt and eta

dependence

• particle level• response, R

• non-linearities• inhomogeneities in detector

response• offset, O

• underlying event• multiple interactions, pileup

• showering, S

• procedure which can be reproduced in both data AND Monte Carlo

• reduce/remove relative systematics

• parton level corrections applied only for some analyses (and analysis-specific)

€

Eptcl =(Eobs −O)

RS

D0: NIM A424:352 (1999)

CDF:

Soft InteractionsSoft Interactions

• underlying event• Et density from ‘spectator’

interactions associated with hard scatter

• somewhat difficult to define

• pileup & multiple interactions• Et signals from out-of-time and in-

time interactions, respectively

• do not contribute to Etmiss• obtained from min-bias data

• CDF measures at different luminosities, difference is systematic (v. small)

• D0 measuires dependence on # primary vertices, luminosity

D0 Run II

CDF Relative ScaleCDF Relative Scale

• Plug region cannot use tracking• use dijet events• trigger jet in range: 0.2<<0.7

• jet fully contained in cone• no corrections applied

• calculate asymmetry variable

• sensitive to eta-dependent variations in• response, showering and gluon radiation

• measure in different Et ranges• Use -jet for systematics€

PTprobe − PT

trigger

0.5 *(PTprobe + PT

trigger )

PlugCentral

cracks

DØ Relative ResponseDØ Relative Response

• studied in direct photon candidates

• sensitive to response• insensitive to physics effects

• EC and CC• similar technology

• might expect similar energy dependence of response

• fit CC and EC together • ECs float within constant

ratio

• inter-cryostat region • remove energy dependence• response vs. eta, in Pt bins

Run I

observed independent of jet E

€

R =1+

v E T

miss • √ n Tγ

ETγ

DØ ShoweringDØ Showering

• particles originating within jet cone• shower/bend • some energy spills out of cone• increasingly important as jets

• point forward• cone sizes < 0.7

• does not contribute to Etmiss

• consider dijet events in data and MC (moving to photon events now)• plot Et density vs. R to jet axis• baseline subtract non-hard Et density

• use Monte Carlo to calculate Et density at particle level• from fragmentation and radiation: subtract from calorimeter

measurement

• systematics• baseline fitting systematics most important• central 1% uncertainty or smaller, 2-2.4: > 5%, increases to 10% at 2.5

D0 Run II

CDF Particle Level ScaleCDF Particle Level Scale

• now available for analysis (i.e. Nov. ‘04)• single particle response

• stable particles, inside jet cone• modelled with Monte Carlo

• E/p modelling primary difficulty in Run II

• for actual detector • low Pt E/p measurement from min-

bias• test beam above 10 GeV• place systematic on Monte Carlo E/p

• jet fragmentation• low momentum tracks very important• data-MC comparison• Herwig/Pythia comparison

• Pythia fragmentation better compared to data

• 1% uncertainty on scale

• total uncertainty @ 50 GeV central jets: 3.5%

• in Central, main error is E/p modelling• in Plug, main error is relative correction

DØ Absolute Hadronic Scale

DØ Absolute Hadronic Scale

• use direct photon candidate events• relative to absolute EM scale

• combine CC and corrected EC data • obtain absolute scale vs. E:

• energy extrema (not yet in Run II)• high energy

• MC similar to data: use it• unbiased low Et jets

• systematics• photon background

• purity to improve dramatically next iteration• topological much less than with pT balancing,

but not negligible• resolution, kT, multiple interactions

€

R = a +b ln E + c(ln E)2

Run II

DØ Overall CorrectionDØ Overall Correction

• parallel analysis in data and Monte Carlo• allows test of whether method succeeded• compare corrected jets to particle level

• corrections • ~ 10 - 15% at high energy• larger in forward and for low Et

• uncertainties• 2% (4.5%) @ 50 GeV in CC for Run I (Run II)

totalcorrection vs. E

&

eta

Run I

Run I after parton corrections

Some Considerations at ATLASSome Considerations at ATLAS

• what algorithms will be used?• cone, kT, energy flow• NN ‘topological clusters’, ...

• how exactly determine cell-level weighting?• sampling weights, H1 algorithm...• if use H1 algorithm, still may have

• low Et cutoff, so a net response correction @ jet level• net Etmiss corrections at object level

• how know tunings translate into calibrated running detector?

• what physics want to do? a very wide range!• very high precision (~0.5% for mtop, ~1% for QCD)?)• much larger kinematic range (to 3 TeV for QCD)• large diversity of physics corrections, when needed• Etmiss calibration is important

• slide 2 considerations seem to hold even more at LHC

in situ Effortsin situ Efforts

• lots of work on physics (parton) corrections• photon, Z + jets (C. Deluca, Barcelona)

• employs pT balance =(pTjet-pTgamma)/pTgamma

• observes net shift of balance• strongly cone size dependent• strong topological effects hamper (at moment)

obtaining correction at pT < 60 GeV• top l+jets; W-> jj sample (D. Pallin)

• if can b-tag: get 10k’s of events• jet-jet opening angle correlated with correction• quote: “No way to extract presice jet absolute calib.

from direct MW rescaling”• physics validation of detector calibration at ~ 5-

10% level

• some issues• top events relatively crowded• W jets not color connected to event• need precision about UE and parton

conventions• flavor-dependent corrections will be important

• W mass from qq’ ”partons” is valuable for internal calibration for top mass measure -- 1% in first yr or two

• Monte Carlo cross checking of jets and their kinematics will be crucial for this effort

DC1

ATLFAST

What Seems Applicable?What Seems Applicable?

• determine detector/accelerator corrections separate from physics

• detector effects in particular should be model-able regardless of physics• excellent detector corrections lay foundation for optimal physics

corrections

• fully in situ methods for jet energy scale measurement• quantify at reconstructed jet level

• obtain from data, • not algorithm/physics specific

Etmiss projection

• parallel particle-centric effort important• to cover reliably the fullest possible kinematic range• completely separate systematics from Etmiss projection methods

• allow for computing of jet corrections relevant for missing Et

• employ procedure which can be reproduced in both data AND Monte Carlo

€

R =1+

v E T

miss • √ n Tγ

ETγ

Near Term Goals (i.e. Rome)Near Term Goals (i.e. Rome)

• use Etmiss to extract response• relative to electron scale• response vs. eta in Pt bins• define physics level diagnostics useful

online• get involved in Event Filter

monitoring infrastructure• readout validation via Level 1

• three different samples needed• photon, Z(ee,) + jet• dijets

bias from jet resolution: - this is important for parton level correction studies, as well

biased

unbiased

define:

Jet Energy Scale in Zee+X

Jet Energy Scale in Zee+X

• Etmiss projection onto Z pT

• use for low to moderate jet pT

• photon+jet for high pT

• need to:• photon and electron resolution• Z pT distributions• establish that pT

Z resolution adequate• understanding Etmiss performance and calculation

Mee

DC2 Z ll via 9.0.1

pT of soft recoil, NOTE: it’s < pT

Z

pT of Z

no algorithm on recoil

Calculating ResponseCalculating Response

• Etmiss ~ 28 GeV, this sample• Etmiss•nT

~ 20 GeV• opposite to Z pT

• non-Z’s have no Etmiss correlation

• Z’s show Rjet ~0.8• A LOT of work to do!

Final CommentsFinal Comments

• prompt hardware validation extremely important• online monitoring with hardware AND physics level diagnostics

• detailed tracking of analog electronics, radiation and other trends• automation compensates for non-expert shift crew• documentation, training, databasing

• understand detector fully as soon in run as possible• calibration, resolution, stability in situ

• express stream Z->ee, samples, high Pt samples• Note: ‘express’ streaming data to get interesting events quick is useless

unless understand detector• be ready with MC tuning work with first data

• ultimately: jet resolution• fragmentation knowledge best way to improve resolution

• especially for non-compensating calorimetry• particle-in-jet method

• different than energy flow -- cone algorithm on towers AND clusters