data-based background predictions for new particle searches at the lhc

Data-based background predictions for new particle searches

at the LHC

David Stuart

Univ. of California, Santa Barbara

Texas A&M SeminarMarch 24, 2010

2

Motivation

Searching for new physics at the LHC.

Potentially fast.With a large step in energy, the LHC could start up with a bang.

3

Motivation

Searching for new physics at the LHC.

Potentially fast.

But many models; on which to bet?

Do they have something in common?

4

Motivation

Searching for new physics at the LHC.

Potentially fast.

But many models; on which to bet?

Do they have something in common?(other than being wrong)

5

Motivation

Searching for new physics at the LHC.

Even within 1 model, many parameters…

Signature driven searches are more general.

But, which signature is best?

6

Motivation

Searching for new physics at the LHC.

Search broadly for any non-SM in all signatures?

7

Motivation

Searching for new physics at the LHC.

Search broadly for any non-SM in all signatures?

But signatures are not precisely predicted.pdfs, higher orders, detector effects…

e.g., Z+jetsq

Z+

-

8

Motivation

Monte Carlo predictions?

Sophisticated, higher order modeling,

e.g., ALPGEN.

Elaborate simulation of detector response.

9

Motivation

Monte Carlo predictions?

Sophisticated, higher order modeling,

e.g., ALPGEN.

Elaborate simulation of detector response.

Both are software…Only trust in so far as validated with data.

10

Motivation

Data validation challenges:

Slow.

Fit away signal?

11

Motivation

Data validation challenges:

Slow.

Fit away signal?

Would be nice to turn off new physics temporarily.

12

A simple discriminator

Most new physics is high mass

Most SM physics is low mass

13

A simple discriminator

Most new physics is high mass Produced at threshold, i.e. at rest. Decay products ≈ isotropic Decay products peaked at zero rapidity

Most SM physics is low mass

Produced ≈ uniform in rapidity

14

A simple discriminator

Validate SM in forward events

and

Search for new physics in central events

15

Start with the Z+jets signature

• Insert favorite model motivation here.

• Clean dilepton signature

• Easy to trigger and reconstruct

• Very little background

A simple signature

16

Start with the Z+jets signature

• Insert favorite model motivation here.

• Clean dilepton signature

• Easy to trigger and reconstruct

• Very little background

…except Z+jets.

A simple signature

17

Z+jets

SM falls ≈ exponentially with NJ.

Signal would appear at large NJ.

18

Forward control sample

SM Z rapidity is ≈ flat since the Z is light.

Forward events are a control sample for ≈ all NJ.

Signal is central.

ALPGEN+Pythia+PYCELL

19

Forward control sample

SM Z rapidity is ≈ flat since the Z is light.

Forward events are a control sample for ≈ all NJ.

Signal is central.After acceptance cuts the conclusion is the same.

20

MethodDefine the fraction of central events with:

R(NJ) = ncentral(NJ) / (ncentral(NJ) + nforwardNJ))

where we define central as |<1 and forward as |>1.3

Measure R(NJ) at low NJ. Extrapolate linear fit to high NJ.

21

MethodPredict number of central events with high NJ as:

ncentral(NJ) = nforward(NJ) * R(NJ) / (1-R(NJ))

From low NJ fit.

{{Measured

Dominant uncertainty is from fluctuations in nforward(NJ).

22

Does it work?Check self consistency in Monte Carlo…

L = 1 fb-1

Predicted

Actual

23

Does it work with signal?Not focused on sensitivity to any specific model,

but using LM4 as a benchmark:

L = 1 fb-1

Predicted w/o signal

Predicted w/ signal

Actual w/ signal

24GeneralizingThe basic premise (low-mass broad rapidity range) generalizes beyond Z’s.

25

Does it work, generally?Check self consistency in each mode…

Predicted

Actual

Z W

multijets

26

Does it work robustly?Check for robustness against mis-modeling. E.g.,

• Eta dependence of lepton efficiencies.• Eta dependence of jet efficiencies.• Changes in higher order Monte Carlo effects.

Expect robustness since data-based prediction:

• Measures lepton efficiencies in the low NJ bins

• Measures jet effects in events with forward Z’s.

• Measures NJ dependence in the fit.

As long as correlations between lepton and jet effects are a slowly varying function of NJ, the R(NJ) fit will account for it.

27

Does it work robustly?Tests with artificially introduced mis-modeling.

Z W j

Alpgen #partons Lepton inefficiencies Jet inefficiencies

Pulls are shown for two highest ET jet bins for each test. Alpgen test = even #partons only and odd #partons only. Lepton test = 30% efficiency changes globally and forward only. Jet test = 30% efficiency changes globally and forward only.

28

R(NJ)

Beyond using R(NJ) to predict the central yield and count events there,

R(NJ) is potentially of general interest as a search variable.

29

R(NJ)The central fraction, R(NJ), is potentially of general interest.

“Minbias” example:

Here, “NJ” uses tracksabove 3 GeV as jet proxies.

The highest pT track is therapidity tag.

R(NJ) ≈ 1/2 because tracks flat in and central ≈ forward

for tracking coverage.

Changing bounds wouldmove R(NJ) but notchange its shape.

R(N

J)

30

R(NJ)The central fraction, R(NJ), is potentially of general interest.

W and Z are light and so similar to Minbias.

Acceptance difference apparent.

31

R(NJ)The central fraction, R(NJ), is potentially of general interest.

W and Z are light and so similar to Minbias.

Acceptance difference apparent.

+jets and jet+jetsare non-flat but still linear.

32

R(NJ)The central fraction, R(NJ), is potentially of general interest.

W and Z are light and so similar to Minbias.

Acceptance difference apparent.

+jets and jet+jetsare non-flat but still linear.

SUSY model points are dominantly central.

33

R(NJ)(-1)

We have also explored another variable that tries to take advantage of the general expectation that the NJ spectrum should be falling.

L = 1 fb-1

Predicted w/o signal

Predicted w/ signal

Actual w/ signal

Without MET cut.

Clear signal when there is an increase with NJ, or even a decrease in the slope.

R(NJ)(-1) = ncentral(NJ) / (ncentral(NJ) + nforward(NJ-1))

34

R(NJ)(-1)

We have also explored another variable that tries to take advantage of the general expectation that the NJ spectrum should be falling.

Z+jetsZ+jets plus LM4

≈ S

35

R(NJ)(-2)

Can “leverage” that to use the forward events from two jet bins previous.

Z+jetsZ+jets plus LM4

≈ S2

This really just represents our generic expectation that for the SM, NJ should ≈ fall exponentially and be uniform in rapidity, while for a heavy particle production is central and increases with NJ. Similar plots can be made for , jet, W.

36

What about Missing ET?

Would like to predict V+jets+MET for a Supersymmetry search.

Is there a SUSY-less sample from which to measure MET?

37

Missing ET in Z+jets

The Z is well measured. The MET comes from the detector’s response to the jet system.

38

Missing ET in Z+jets

For each Z+jet event, find an event w/ a comparable jet system and use its MET as a prediction.

Huge QCD x-section makes such events SUSY free.

39

Missing ET in Z+jets

For each Z+jet event, use a MET template measured from events with a comparable jet system in O(1) pb-1.

Templates measured in bins of NJ and JT = j ET.

40

Missing ET in Z+jetsExample of template parameterization

Background predictionData distribution

For each data event...

41

Missing ET in Z+jetsExample of template parameterization

Background predictionData distribution

For each data event,look up the appropriate template.Sum these, each withunit normalization, to get the fullbackground prediction

N JETSpT>50 GeV

sumETBin 1

sumET Bin 2

sumETBin 3

sumET Bin 4…

2

3

4…

42

Missing ET in Z+jets, MC closure test

43

Missing ET in Z+jets, MC closure test

44

Missing ET in Z+jets, MC closure test

45

Missing ET in Z+jets, MC closure tests

“Scaled” includes a low MET normalization, which is important for low NJ.

46

Missing ET in +jets, MC closure test

47

Missing ET in +jets, MC closure test

48

Missing ET in +jets, MC closure test

49

Missing ET in +jets, MC closure tests

“Scaled” includes a low MET normalization, which is important for low NJ.

50

Missing ET in Z/+jets, robustness tests

Various detectoreffects could addMET tails.

Check robustnesswith MC tests,applied equally toall samples.

51

Missing ET in Z/+jets, robustness tests

• R=0.8 hole at (h,f)=(0,0)• Double gaussian smearing• Randomly add 50-100 GeV “noise jets”• Vary nJ slope by ±50%.• Jet energy scale sensitivity.

52

Missing ET in W()+jetsW

Can predict W+jets, with forward/central,

but not ttW+jets

because top is heavy.

53

Missing ET in W()+jetsW

Templates can predict the fake MET in W+jet events, but we also need to predict the real MET, i.e., the pT.

Can predict W+jets, with forward/central,

but not ttW+jets

because top is heavy.

54

Missing ET in W(+jetsW

Templates can predict the fake MET in W+jet events, but we also need to predict the real MET, i.e., the pT.

But, pT spectrum is ≈ same as pT spectrum,

if we ignore V-A or randomize W polarization.

Can predict W+jets, with forward/central,

but not ttW+jets

because top is heavy.

55

Missing ET in +jets

Pretend we could detect and apply templates.

Mismatch due to b-jet dominance.

But, neutrino pT dominates MET.

56

Missing ET in +jets

Combining template prediction with pT spectrum

gives a prediction for the full MET distribution.

57

Missing ET in +jets

The same approach predicts W shape,

if polarization is random.

58

Comparison with signal

Benchmark points (LM4 and LM1) stand out with 200/pb at 14 TeV.LM4=(m0=210,m1/2=285); LM1=(60,250). tan()=10.

59

SummaryExplored data-based background predictions that avoid reliance on MC.

Rapidity is a simple discriminator that relies only on kinematics.

It provides a data-based background prediction that:

• Avoids generator and detector modeling uncertainties by measuring a ratio.

• Fails to discover anything that it shouldn’t, even when reality bites.

QCD based templating gives an in situ prediction of MET distribution.

Charged lepton pT predicts neutrino pT.

We will validate these methods with low NJ data soon.

Work done by Victor Pavlunin. More details are available in:PRD78:035012 arXiv:0806.2338 & PRD81:035005 arXiv:0906.5016

Standard Model backgrounds to Z+jets

+ +

+q Z

e+

e-