Jets and Missing ET at the Tevatron

Andy Haas

Columbia University
DØ/ATLAS

US-ATLAS Beyond the SM Workshop

LBL

Jan. 10-13, 2006

Thanks to Daniel Whiteson

and Florencia Canelli from CDF

Fermilab Tevatron Accelerator

1 km radius p-pbar super-syncrotron

Energy: sqrt(s) = 1.96 TeV

Peak inst. luminosity: 1.7e32 /cm2/s

Integrated luminosity: 1/fb

36 bunches of p and pbar

396 ns crossing period

Two 25 cm RMS interaction regions

CDF and DØ: multi-purpose detectors

~600 physicists each

LAr / Scintillator, hermetic calorimetry

Nearly compensating / non-compensating

Inner trackers w/ silicon vertex

Full simulation: Pythia/Herwig, GEANT3

Basically an LHC 10% test!

Plenty of jets to study, up to pT=~500 GeV

Missing ET, up to ~200 GeV

So, what has been learned about jets and mET?

Outline

How well does the MC resemble the data?

How do you make the MC resemble the data?

MC tuning / scaling

Data quality issues

Jet energy scale and resolution

Trigger effects

A look at some data/MC comparisons

Perturbative QCD data/MC comparisons

LO parton generators: multijets, heavy-flavor

Some fancier jet corrections

Hadronic calorimeter calibration

Using tracks to improve jet energy resolution

How are jets and mET used in physics analyses?

Different algorithm and correction choices

When are “QCD” backgrounds simulated or taken from data?

A look at a few DØ jets + mET analyses

MET significance : a per-event mET correction

Simulations of Jets / mET Compared to Data

Every MC simulation is already tuned to some experimental data

String-theory just isn't that advanced yet!

Even when it is, input will still be needed to determine our particular ground-state :)

The Tevatron cheated, it had Run I

Even when used “out of the box” now,
PDFs / generators / fragmentation / decay, are partially pre-tuned to Tevatron data

How different were the initial simulations from the first Run I data?

I don't know! I was 12 years old. :)

Will initial LHC simulations do a similarly good job of modeling LHC data?
I think it's hard to compare the situation in the early '90s to 2007...

Better theoretical QCD techniques

Better simulations and software

Beyond this basic tuning, both CDF and DØ have moved forward and performed more detailed tuning and corrections

Underlying event / pile-up / noise

Single-particle response (EM and Had)

Scaling / smearing, where necessary (yuck!)

The first year of LHC running, at low luminosity, will be a critical time for collecting data needed to tune the simulation!

Underlying Event and Pile-up

UE / Pile-up is very important to tune

Affects jet energy and mET resolutions
(as well as lepton isolation and other effects we won't discuss in this talk...)

Must adjust MC parameters such that the simulated UE resembles data as closely as possible

Tevatron uses Pythia “tune A” - by CDF

Can do the same for pile-up (minimum-bias events) - CDF
or:
Add zero-bias events from data – DØ recently switched to this approach

Data is a better idea in principle, but harder, due to data quality and technical issues

Very important to match the amount of pile-up in MC to the luminosity profile of the data!

Critical for getting good data/MC agreement in mET resolution, etc.

This is easier to do with zero-bias data

Detector noise is also tuned to data

And corrected for in jet energy scale

The DØ “offset” energy vs. |eta|

from UE and pile-up at

several inst. luminosities

Z->ee data

MC+0bias_data

MC+MCpileup

mET

Single-particle Tuning

For the MC to represent the data even better, the response of the calorimeter to single particles (pions) can be measured in data and then corrected for in the MC

This was made more difficult by the lack of a dedicated test-beam for the DØ Run II calorimeter electronics!

CDF has fully incorporated these corrections into their MC simulation

A dedicated trigger regularly takes single-pion data

Critical for their small jet energy scale uncertainty

DØ uses single-particle response data to correct jet energies on a jet-by-jet basis, based on track information

The trigger has not yet been optimized

Difficult to obtain a clean single-pion data sample with large statistics and understand it

Study both gamma+jet events with an isolated track (in data and MC) and also single-pion MC

Require track and calorimeter isolation

Cuts on EM fraction

DØ

Single-pion

Cal. E (GeV)

CDF: Track E/p

Data/tuned-MC

Scaling and Smearing

An experiment never wants to do this... :)

But sometimes it has to!

MC must be smeared and scaled until the simulations themselves can be tuned properly

It's likely that ATLAS will have to make these kinds of rough corrections in the early days of data taking, while MC is being tuned

DØ corrects for:

Jet-id efficiency at low-pT

Jet energy resolution

mET in good agreement now at DØ after adding real zero-bias for pile-up in MC

It is partially understood why these are not modeled well in simulations

Hadronic response is too high in MC, because it doesn't simulate the shortened charge-collection time (420ns) in Run II electronics correctly

CDF does not have to make any object-level jet or mET corrections!

Data Jet ET (GeV)

DØ

Jet ID data/MC

scale factor

Data Quality

Missing ET is monitored online and studied offline

By run

By 1-minute blocks

Fake mET comes from:

Detector problems

External RF noise / grounding problems

Hardware failures (can be detected online but must still be flagged offline)

Replaced / repaired hardware

Improper online calibrations

“Physics” effects

Beam losses

Halo muons

Cosmics

Problems are detected offline and corrected for, or events flagged as “bad”

The fraction of “physics events” which are flagged bad is also measured in data – and then corrected for in MC

DØ

CDF

Jet Energy Scale

Jet energies are corrected for:

overlap from the underlying event

depends on tower |eta|

overlap from other ppbar interactions

depends on |eta|, nPV

out-of-cone showering

depends on ET

calorimeter response

depends on ET, |eta|

mET is corrected for this!

muons in the jet, for b-jets

Use data gamma+jet events, assuming ET balance

CDF focuses more on the relative differences between JES in data compared to MC

DØ has developed absolute JES for data and MC separately, but will soon also have a relative scale



leading jet

ET



DØ jet response vs. E

Jet Energy Scale and Resolution

Systematic uncertainty is estimated by varying many analysis parameters

photon ID cuts

event topology (dphi)

PV selection

Seeing where data and MC disagree (CDF)

Systematics are also added for the fact that gamma+jet does not agree with Z+jet !

Now have made a preliminary JES measurement using ttbar->W(lnu)+W(jets)

Can also calibrate the b-jet energy scale

Resolution is studied in gamma+jet and di-jet events (to extend to higher ET) and compared to MC

DØ Run II jet resolution is worse than Run I: more material in front of the calorimeter

Z->bb would be a great cross-check of jet energy scale and resolution for b-jets

Very difficult analysis!

But some preliminary results exist

DØ jet resolution

Trigger Simulation

Needed for studying hypothetical new triggers

Needed for optimizing trigger lists for best efficiency at a given rate

Needed for analysis: model the effect on efficiency and kinematics of simulated MC signals and backgrounds

Almost all analyses at CDF and DØ measure “turn-on” curves in data, and apply them to MC jets and mET objects

Sometimes several turn-ons are combined in a parameterization to model a single term

More accurate than the trigger simulations

Care has not been taken to calibrate the trigger-level quantities in the simulation

It's a difficult job!

The simulations of the algorithms often have bugs or do not reproduce data for some reason

Trigger conditions are often different for various sub-sets of a data-set

A proper simulation would require an extensive database infrastructure

Much easier to deal with this “in situ”

DØ L3 jet turn-on

DØ L1 data

compared to

parameterized

simulation

Perturbative QCD Data/MC Comparisons

How well have we done? Does MC now look like our data?

Look at the dphi between the two leading jets (“angular decorrelation”)

A measure of the jet “radiation”

Also a good test of PDFs

Use a cone jet with R=0.7

Apply JES corrections

Restrict to the central |eta| region, <0.5

Compare data to:

LO pQCD (does not describe low and high dphi)

NLO pQCD (does a good job)

Pythia and Herwig showering algorithms

Herwig is good “out of the box”

Pythia needs a little more ISR... but is within tuning uncertainties

Parton Generators: Data/MC

Jets and mET in analyses often suffer from higher-order processes:

Multi-jet events, W/Z+jets

Heavy-flavor production / fragmentation

Modern generators do a very good job

ALPGEN, etc.

Jet parton-shower matching algorithms

Pythia fragmentation, gluon splitting

Can get correct: nJets, pT spectra, eta distributions / correlations, heavy-flavor fractions

But!	Use data where possible

“QCD” here is from “matrix method” - inverting lepton id cuts in data

A huge amount of MC QCD would be required to model this background

Always tune / scale processes to data

~+20% are common “k-factors” - just one is enough for W+jets...

Heavy-flavor fraction is well-modeled!

Jet 0 ET

Jet 1 ET

Jet 3 ET

Jet 2 ET

Jet dR
(0,1)

Num. jet
after b-tag

Other Calibrations

Beyond the basic cone-jet algorithm, several methods have been tested for improving jet energy resolution

Both experiments have implemented the kT algorithm – no major gains seen... not used in any analysis I know of...

DØ: TrackCalJet – 10-15% improvement

Use the single-pion response functions measured in data and MC to improve the jet response – a kind of “energy flow” algorithm

Both experiments have also devoted significant effort to calibrating the calorimeters in situ

For EM layers, use Z->ee

For Hadronic layers:

Use single particles for inter-phi calibrations (take advantage of azimuthal symmetry of beam / detector)

DØ: Use di-jet events to calibrate inter-eta / inter-layer -> 5% improvement in jet energy resolution

CDF: H1-type calibrations

How big is the improvement?

Were there any surprises seen or interesting lessons learned?

DØ jet E res.

with TrackCalJet

DØ hadronic eta

calibrations

Analysis-specific Algorithm Choices

Jet cone sizes are sometimes different between analyses, but are mainly historic choices

Real differences in terms of resolution or systematics are minimal

Top / SUSY / Higgs tends to use smaller cone (.5 at DØ, .4 at CDF), because of busier event

QCD uses 0.7 cone

Require jets to come from the same primary vertex (charged-particle-fraction cut) ?

Also has an impact on the mET (E sin)

Seems sensible, but can induce larger systematics due to min-bias modeling

Apply track-based jet energy corrections?

This has better resolution, but possibly will have larger systematic uncertainty on scale

There are also differences in the mET algorithms / corrections used

Do you reject events with possibly mismeasured leptons (in cracks)?

Reject mET aligned with jets and/or leptons?

Cutting on CPF removes jets from pile-up



An Example Jets+mET Analysis

The search for squarks and gluinos looks for excessive mET in events with at least two jets

Fake mET is suppressed in di-jet events by requiring them to not be back-to-back, dphi<165

Note that “QCD” is not simulated: It's measured in data and extrapolated under the signal region, where it's determined to be negligible

The shape of the “SM” W+jets, and Z(nunu)+jets, etc. reproduces the data

Extra selections are added for the sbottom search, which later adds b-tagging

Lepton vetoes and requiring mET isolation from jets and leptons reduce the QCD dramatically, and also the W+jets

Mono-jet search is challenging!

Very sensitive to JES, at high jet ET

Sensitive to cosmic and beam backgrounds

No approved plots yet, sorry!

dphi<165

mET>40 GeV

And lepton vetoes

And dphi(mET,jet)>35

Search for Single-Top in Leptons+Jets+mET

The search for single-top production looks at the kinematics of W(lnu)+b+jet events and compares to MC simulations

So called “triangle cuts” in planes of:

mET vs. dphi(mET,jet[0,1])

mET vs. dphi(mET,lepton)

Triangle cuts allow for a more efficient selection of signal, while rejecting fake mET background

Good agreement in mET both before and after b-tagging!

Note that again, “QCD” is taken from data, but W/Z+jets and ttbar is from ALPGEN+Pythia MC simulation

b-tag

H->WW: mET Significance

The search for SM Higgs->WW looks for an invariant mass bump in the WW spectrum

Very important to reduce non-WW background such as Z(->ee)+ jets

Purify the mET measurement by:

dphi cuts w.r.t. jets and leptons

remove events with jets going into poorly instrumented regions (calorimeter cracks)

Also use “mET significance”, a continuous quantity based on the mET resolution for each event, based on the objects in the event

Sum over degradation of the mET from jets, leptons, and unclustered energy

A mET significance cut has better S/sqrt(B) than a simple mET cut

Also useful for SUSY searches!

Conclusions

Jets and mET are studied at the Tevatron in a somewhat LHC-like environment

But, relatively low radiation / luminosity

Little attempt made at reconstruction of jets past |eta|>2.5

MC does a good job of reproducing Tevatron data...

But, only after careful tuning, corrections, and normalizations

And it's still preferable to use data for “QCD” backgrounds where possible

Building a calorimeter with excellent data quality and low noise will make the data agree with the MC more easily, as well as simplify all jet and mET calibrations!

We've learned how to measure the jet energy scale using gamma/Z+jet and have started testing fancier methods like Z->bb and ttbar

We've applied several advanced calibration techniques: di-jet eta intercalibration, H1, and track-based methods for pile-up rejection and calorimeter response corrections

Analyses use several different jet and mET corrections, depending on whether they are needed and don't induce unwanted systematic uncertainty

For mET: require acoplanar jets, reject “crack” events, require mET “isolation” from jets and leptons, use “triangle cuts”, cut on mET significance

Finally, another interesting way the Tevatron could help... :)

Backup slides...

Jet Reconstruction

The ET in (0.1x0.1)(-) towers is summed, and seeds are found

Energy is clustered in cones of ∆R=sqrt(∆2+∆2)=0.5 around the seeds

The midpoints (in -) between stable cones are also used as seeds

The unique cones with
ET>8 GeV are merged or split, depending on whether they share more or less than 50% of a jet's ET

Jets' ET are calibrated, using the jet energy scale (JES)

accounts for out-of-cone showering and the underlying event, on average

EM

Hadronic

MET

A Quadruple b-tagged Event

Andy Haas

2004-07-07T15:59:13