Jon Butterworth, UCL 1 Jonathan Butterworth MC4LHC/MCnet/HEPTOOLS/Artemis training event 5 th & 6 th...

Jon Butterworth, UCL 1

Jonathan Butterworth

MC4LHC/MCnet/HEPTOOLS/Artemis training event

5th & 6th Jan 2009

Thanks to K.McFarland, E. Nurse, P.Renton, F. Siegert, R.Thorne…

Experimental Techniques and their Impact on

Phenomenological AnalysesPart 1


Outline

• Introduction

• Fundamental or observable?

• Corrections, corrections

• Jets and Jet Algorithms

• Leptons and photons

• Inclusive or exclusive

• Monte Carlos and tuning

• Inconclusion


Introduction

• The aim of these lectures is to discuss how choices

which are made by experimentalists have an impact on

the usefulness of the data for phenomenological

analysis.

• Seemingly arbitrary choices can sometimes

dramatically affect the shelf-life and impact of a

measurement!


Fundamental or Observable

“Fundamental” things (top, W, H masses, couplings etc) are often only defined

within the theory, and so are often rather model dependent, whereas

“observable” things (proton mass, charged particle multiplicity, inclusive lepton

cross section etc) are well defined but difficult to interpret.

Jon Butterworth, UCL 5Pete Renton Jul/ Aug 2008

Precision (pseudo-)observables

ll FBhad

0hZ A M

tau polarisation - Ae(1)

left-right asymm -Ae (1)

Z lineshape (5)

Z (b,c) properties (6) cbcFB

bFB

0c

0b A A A A R R

W properties (2)

top quark mass (1)

WW M Total of 17 at high Q2

(assuming lepton universality)

from > 1000 measurements with (correlated) uncertainties

2 lept hadeff FBsin (Q ) (1)

Jon Butterworth, UCL 6ICHEP08 Pete Renton Jul/ Aug 2008

progress vs. time

2008 direct mt and mw

2008 indirect mt and mw

1995 indirect mt and mw

1995 direct mt and mw

Pete Renton 2008





• Note that H1 and

ZEUS do not even plot and fit F2 here;

• The reduced cross

section is an obserable

(modulo electroweak corrections), F2 is not.



• Parton densities are

transportable to other

experiments within the SM

(QCD, factorization).

• Good example of experiments

making measurements and

doing the phenomenological

analysis themselves, but also

making the data available to

others (MSTW, CTEQ, NNPDF

etc…)



• So the biggest headline derived from a measurement is

not the same thing as the measurement itself– It is often not even really a measurement.

• Ask yourself: if – (e.g.) the LHC proves that the SM is wrong in some possibly

bizarre way (no Higgs in loops, lots of KK particles in loops, low mass gluino or gravitino)

– Or some theorist makes a much better calculation of my process

how does a given result need to be modified?– For a real measurement, the answer should be “it doesn’t” or at

least “it doesn’t beyond the systematic uncertainties”.


Corrections, corrections

• Many corrections are or may be applied on the way to

publishing a measurement;– Dead time, trigger acceptance, detector geometry, energy

scale, dead material, pile-up, electroweak radiative corrections, FL, “underlying event”, “hadronisation”…

• If a correction requires intimate knowledge of the detector,

then apply it.– After our experiment finishes, no-one is going to be able to (or

want to!) correct for our trigger efficiencies, energy scale and resolution etc



• If a correction requires intimate knowledge of a developing

theory, or a Monte Carlo model of some poorly-known

physics, do not apply it!*– After your experiment finishes, if someone does a NNLO

calculation, or really understands underlying events, hopefully your data remain useful!

• There’s obviously a sliding scale, and a grey area…– Dead time, trigger acceptance, detector geometry, energy


*or at very least, present the uncorrected version first!



• If a correction requires intimate knowledge of a developing

theory, or a Monte Carlo model of some poorly-known

physics, do not apply it!– After your experiment finishes, if someone does a NNLO

calculation, or really understands underlying events, hopefully your data remain useful!

• There’s obviously a sliding scale, and a grey area*…– Dead time, trigger acceptance, detector geometry, energy


*although personally I don’t think it’s very big!



• So it is best to have both (a la HERA DIS: cross sections

and PDFs), but if you are only going to have one it must be

the observable!

• Few, if any, measurements are completely model-

independent.– There will probably be some model dependence in our

detector corrections (e.g different hadronisation models affect how we understand calorimeter response)

– Minimise it and include in systematic error bars.



• An example of something in the grey area

• W-asymmetry or lepton asymmetry at Tevatron?

• next slides slides from Kevin McFarland (Rochester)

DIS08













• W-asymmetry or lepton asymmetry at Tevatron?

• Previous slides slides from Kevin McFarland (Rochester)

DIS08

• W asymmetry has a more direct relationship to the

distribution of particular partons

– And it contains real extra information (from the missing pT)

• BUT sensitivity to sea quarks is lost and replaced by a

systematic error– Easier for PDF fitters to fit the lepton asymmetry.

• Similar issues at LHC (less severe if one assumes anti-u

and anti-d converge at low x.


Jets and Jet Algorithms

• What is a jet?

• Some requirements on jet definitions

• Available algorithms


What is a Jet?• Protons are made up of quarks and gluons.

• Quarks and gluons are coloured and confined – we only ever see hadrons.

• A jet of hadrons is the signature of a quark or gluon in the final state.

• The gross properties (energy, momentum) reflect the properties of the quark

or gluon, and stand out above the rest of the event.

• Jets have a complex substructure.

Proton Proton

High PT Jet Production

PT(hard)

Outgoing Parton

Outgoing Parton

Underlying Event Underlying Event

Final-State Radiation

Initial-State Radiation

Picture from Rick Field


What is a Jet?

• Evolution from a hard parton to a jet of partons takes

place in a regime where:

– Energy scale is high enough to use perturbation theory

– x is not very small

– Collinear logarithms are large

– Multiplicities can be large

– This is largely understood QCD, and can be calculated

• Hadronisation (non-perturbative) stage has a small

effect (sub-GeV level) and is well modelled by tuned

Monte Carlo simulation (e.g. Lund string)


What is a Jet?

• Jets are not just less-well-measured leptons or “smeared”

partons.

– Hard radiation interference at amplitude level

– Matching at high scales with Matrix element

– Matching at low scales with parton densities and hadronisation model

– potentially useful information in the internal jet structure, and in particle/energy flow between the jets

• Jets have no existence independent of the algorithm

– even if the “algorithm” = event display + physicist


What is a Jet?

• So jet algorithms don’t so much find a pre-existing jet as define

one.

• A “jet” (or a pattern of jets) is a complex QCD event shape,

designed to reflect as closely as possible the short distance

degrees of freedom (quarks, gluons, H, Z, W…)

– The degrees of freedom themselves are generally not physical observables, but can only be extracted within some theory or model

– The cross section for quark production in the final state at LHC is zero (unless we find something very exciting…)


What is a Jet? (continued)

• We want to connect what we measure back to the fundamental degrees

of freedom of our (Standard) model, so we can publish for example:

– Parton densities

– Top, W, (Higgs?) masses

– Deviations from the Standard Model (or limits derived from their absence)

• (Usually) requires comparison to state-of-the-art theory, so our jet

finding procedure had better be something the theory/model can

replicate

– Clear separation between detector corrections (model independent) and interpretation (model dependent).

• “Truth” is algorithm+final state, not the calorimeter and not “partons”


Example (from my own experience)

• Jet photoproduction at HERA

– Was a completely new energy regime: Photoproduction of jets never before observed (some analogy to what we are about to embark on)

• ZEUS tried kT cluster, plus two implementations of “Snowmass” cone

algorithms

– One based on a sliding window, one based on a seed. Both respectable at the time (the seeded one was based on the CDF algorithm of the time)

• NLO (Klasen, Kramer) drew cones around partons (max three per event)

– Two partons separated by R ~ 2 may still be in a single cone of R=1, but may not be found if there is no seed in between them.

– Introduce Rsep, the maximum separation allowed between two partons (Ellis, Kunszt & Soper 1992).


Example • Rsep has a large effect on the NLO theory (left)

• This has no analogy in a final state jet algorithm.

• No common jet definition possible.

• Difference between cone algorithms has large effect on the experiment

(right)

– NLO theory cannot use either of ZEUS’s IR unsafe cone algorithms

From JMB, L.Feld, G.Kramer, M.Klasen, hep-ph/9608481


Example

• Neither theory and experiment knows how reproduce the other

– Actually they can be made to agree by tweaking RSEP

• Predictive power of NLO QCD practically gone

• Not a problem if both use the same algorithm

– in the ZEUS case we went with kT

From JMB, L.Feld, G.Kramer, M.Klasen, hep-ph/9608481


What is a Jet?

• One good place to look: Les Houches 2007 accords (arXiv:0803.0678)

• Jet definition specifies all details of the procedure by which an arbitrary

set of four-momenta is mapped into a set of jets. Composed of:

– Jet algorithm (e.g inclusive, longitudinally boost-invariant kT).

– All the parameters of the jet algorithm (e.g. R=0.7)

– The recombination scheme (e.g four-vector recombination, or E-scheme, or…)


What is a Jet?

• One good place to look: Les Houches 2007 accords (arXiv:0803.0678)

• Final-state truth-level specification

– What is the input (at the truth level). e.g. all final state particles with lifetimes longer than some cut…

– Theory and experiment can correct to this level, within some controlled systematic error.


Jets for different applications

• If you are doing simple searches at high pT, jets are obvious

– …maybe

– Certainly even the “event display + physicist” algorithm should be pretty reproducible for counting ~1 TeV jets at LHC • (really? How precise is that 1 TeV cut?)

• If you are making precision measurements of jet cross sections

(energy scale ~1%) then you will need to compare at least to NLO

QCD.

– In this case it is mandatory to have a jet definition which can also be applied to NLO QCD

• Consequence: This means a detector-independent, infrared &

collinear safe algorithm


Infrared Safety?

• Adding an arbitrarily soft gluon to the event should not change the jets.

• Non-infrared-safe jet algorithms cannot be used in higher order

calculations.

• so if we use them in the experiment we cannot compare to the best theory

(at least without making some intermediate model-dependent correction).

• Actually, infrared instabilities undermine the claim of a jet algorithm to be

telling us about the short distance physics.

• We should also worry about sensitivity to arbitrarily low noise, or arbitrarily

soft pions, for example.

• An example of an infrared instability is the “seed” in old cone algorithm.

But there are others (e.g. in the splitting/merging)


Precision Application• ZEUS Jet measurements

• 1% energy scale, kT algorithm

• Compared to NLO QCD, used in NLO

PDF fits

Extrapolation: A. Cooper-Sarkar,

C.Gwenlan, C.Targett-Adams, HERA-

LHC Workshop, hep-ph/0509220



• Our “hard” events will be complicated. Many hard jets, minijet vetos and

rapidity gaps. Several different hard scales in the event (pT, MW, Mt…

Mplanck???)

– This is when QCD background calculations get really hard, and sensitivity to soft effects and higher orders becomes critical.

– Clearly we will take as much background information as we can from the data, but equally clearly there will still be a need for stable jet definitions, and for some modelling and comparison with theory

• e.g. extrapolating the W pT from the Z pT at the Tevatron

• Consequence: Reiterates need for a reproducible & stable definition

• Consequence: Our jet algorithms must be pretty fast even for very high

multiplicities



• Our the environment our “hard” events are in will be complicated.

– Underlying event and pile-up, not to mention experimental noise & efficiency

• Consequence: Our jet algorithms need to be good at picking out the

“hard” physics.

– Not just “reproducible” underlying event corrections, but small ones



• Our high pT jets will sometimes have stuff in them (High pT top, W, Z,

Higgs, SUSY particles…)

• Consequence: Useful to have an algorithm which can be “unpicked”,

has a recombination scheme which conserves energy & momentum,

and is stable against soft physics.


Available Algorithms

• “Cone” algorithms

– Generally seek to find geometric regions which maximise the momentum in a given region (often not actually a cone!)

– Sort of mimics the “event display + eye” method

• “Cluster” algorithms

– Generally start from the smallest objects available, and perform an iterative pair-wise clustering to build larger objects (using either geometric or kinematic properties of the objects)

– Sort of inverts the QCD parton shower idea


Recombination Schemes

• In principle can choose how to calculate the momentum of a (pseudo)

jet from its constituents

• In practice, two main possibilities used:

• Snowmass/ET scheme

– ET = S(ETi) ; h = S(Et

i hi)/S(ET)

– Forces massless jets from massless particles (pseudorapidity=rapidity, ET = pT)

• Four-vector addition

– Preserves mass information, although normally starts with massless objects

– Care needed (pT or ET, h or y?)


Cone algorithms

• “Simple” (snowmass) cone algorithm not fully specified

– Many subtly different algorithms go under the name “cone algorithm” (with or without the Snowmass badge).

– Often re-implemented and changed within each experiment, which is bad for inter-experiment comparison and for reproducibility.

– Solved by using an external package, e.g. fastJet (Cacciari & Salam)

• Iterative

– Find a cone,

– Calculate the centroid

– Redraw cone around the new centre

– Recalculate centroid

– Etc (look for stable solutions)


Cone algorithms• If they rely on a seed to start cone finding, and many older ones did, they

are not infrared safe.

– Using midpoints of seeds as well helps, but only postpones the problem to the next highest order.

– Seedless cone algorithms (e.g. SiSCone, Salam & Soyez) now available.

• “Progressive Removal” type (as current in CMS)

– start with the highest momentum seed (not colinear or IR safe!)

– Find a stable cone and remove it

– Continue

• Split/merge (as is the current “ATLAS cone”)

– Find all stable cones.

– If two stable cones overlap, merge if the overlap is > X (~ 50-75%) . Otherwise assign energy to the higher pT one.


Cone algorithms

• Geometrical localisation is appealing when mapping on to a real detector

(which is of course geometrically localised!)

– Although note, the overlap issue means that the “cone” is not in fact regular, especially in high multiplicity events.

– (Can be very large, in fact, depending on the split/merge treatment)

– But its edges are fairly sharp.

• SiSCone does now provide a practical (i.e. fast enough) infrared safe &

seedless cone algorithm in public code

– Used a sliding window idea to find all possible stable cones

– Has a split-merge step

– arXiv:0704.0292[hep-ph], Salam & Soyez


Cluster algorithms• Again, many possibilities

– Jade, kT, Cambridge/Aachen, Anti-kT , …

– Each has a distance measure, and merges the “closest” objects by this measure until some criteria is reached (could be a specified multiplicity, or “distance”)

• Modern ones (kT, Cambridge, anti-kT) belong to a general class where

the distance parameter is given as

– p=1 for kT, 0 for Cam/Aachen, -1 for anti-kT

• Can be very slow at high multiplicities

– recently solved in fastJet (Cacciari & Salam, Phys.Lett.B641:57-61,2006)

• No seed needed

• All objects assigned uniquely to one jet (no separate split/merge stage)


kT algorithm

• Catani et al Phys Lett B269 (1991); Nucl. Phys. B406 (1993); Ellis and

Soper Phys Rev D48 (1993).

• p=1

• Successively merge objects with low relative kT

• If the kT2 of an object w.r.t the beam is lower than kT

2 w.r.t anything else in

the event divided by R2, don’t merge any more; call it a jet.

• Mimics (inverts) the QCD parton shower.

• Soft stuff merged into the nearest hard stuff.

• Can undo merging (Y splitter). Last merge is the hardest.


Cambridge/Aachen algorithm• Dokshitzer, Leder, Morretti, Webber (JHEP 08 (1997) 01; Wobisch and

Wengler hep-ph/9907280

• p=0

• Successively merge objects with low relative .D

• Objects with D2 > R2 not merged

• Can undo merging. Last merge is the hardest. Last merge is the furthest

away (so is often the softest).


Anti-kT algorithm

• Cacciari, Salam, Soyez JHEP 0804:063,2008

• p=-1

• Successively merge objects with high relative kT

• dij is determined soley by the kT of the harder of i & j, and by D. Soft stuff

within R2 of a high kT object will be merged with it. If two hard jets are

close the energy will be shared based on D.

• Shape of jet is unaffected by soft radiation.

• Can undo merging (but I don’t yet understand quite what it would mean).


Scales in the experiment• (1) Proton mass / becomes possible to accurately calculate using

perturbative QCD● around 1 - 5 GeV.

• (2) W, Z mass / electroweak symmetry-breaking scale / Higgs

mass if it exists● around 50-300 GeV

• (3) LHC scale O(10 TeV).

• Because of (3) the LHC is the first machine to produce copious

highly-boosted particles with masses at scale 2, or very high

multiplicities of jets each at scale 2.


Summary for Jets

• We know a LOT more about QCD and jets now than

we did before LEP, HERA and the Tevatron– Lots of important interaction between experiment and

phenomenology at these machines

• The LHC is going into qualitatively unknown territory

for QCD, never mind for everything else.

• We should take the best technology with us.


Leptons and Photons

• Some issues similar to jets – Isolation from hadronic activity: define “activity”

– Electrons in e+e-/DIS: QED initial state radiation• “Radiative return” to the Z peak.

– Electrons anywhere: QED final state radiation• How photons were radiated?

• Were they included in the electron object?

• Best not to have an implicit cut based on your calorimeter geometry.


Leptons and Photons• In a MC comparison, take the “true”

electron– Large sensitivity to FSR

• Include photons within dR<0.2 of the

electron– Reduced sensitivity

• But what does the measurement

correspond to?


Inclusive or exclusive

• An inclusive “measurement” is often easy for the theorist,

but misleading

• More exclusive observables are harder to calculate

predictions for but experimentally honest

• In general, avoid integrating into “different” regions where you have zero sensitivity (i.e. Φ is generally ok but not pT)


Inclusive or exclusive

• Many examples where errors new measurement just about bracket the

theory uncertainties: because they are the SAME THING!

– e.g. early ZEUS D* measurement extrapolated to high y and low pT

– Or measured with jets, differential, in a region of good acceptance

Nucl.Phys.B729:492-525,2005

NLO from Frixione, Mangano, Nason, Ridolfi


Rivet, robustness and tuning

• Rivet: “Robust Independent Validation of Experiment at

Theory”

• If you can’t write a Analysis routine for it, it’s probably

not a measurement.

• Advert -> Hendrik Hoeth’s talk. (Or Mike’s)


Inconclusion

The separation between theory and experiment is not as clear cut as one might think.

Clarity on the difference and as much separation as possible is desirable in the measuring and analysing of data

Less separation is desirable between the communities working on these things!

Points of clear contact: jet definitions, Monte Carlo and exclusive calculations, PDF fits. EW effects may rise again!)

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Jon Butterworth, UCL 1 Jonathan Butterworth MC4LHC/MCnet/HEPTOOLS/Artemis training event 5 th & 6 th...

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