# Presentation: 472876

## Slide 1

## Point 1: The list of potential models is endless...

-                       and each has many variations, parameters..
-                              and several could be occurring simultaneously...

- Illustration by Hitoshi Murayama

## Slide 3

## We are experimentalists, and should be doing experiments

## ♦ that’s the way we have always done it

- But, but ...
- ♦ that’s the way we have always done it
- ♦ without a model there is no motivation
- ♦ model helps tell a story in the paper and presentations
- ♦ model limits advance the theory knowledge
- ♦ strictly optimized searches are good training for students
- ♦ while covering models we can
-   in parasitically cover all signatures
- ♦ lets us compare with DØ

## Virtually all of the basic techniques 

- Virtually all of the basic techniques
-      used here are the same as other high-pt searches,
-          just automated and scaled up
- Statistical evaluations are precise
-        and include trials factors
- We are not blind – we constantly look
-       at all the data and iterate

## Slide 7

## Slide 8

##     All data are treated as both signal and control

-     All data are treated as both signal and control
-        *one person’s control region is other’s signal region*
*            **...**many, many effective control regions!*
-    Goal is to identify a discrepancy on which we can
-               base a new physics claim
-               NOT obtain a perfect description of data
-     Nothing gets cut away
-     Keep model simple
-     Focus on discrepancies,
-             ask: is there a mundane explanation?
-     Require any change is physically motivated and
-         improves overall agreement

## 927 pb-1

- 927 pb-1

## Slide 11

## Slide 12

## 1) Basis of all predictions is Monte Carlo

- 2) Allow simulation to predict fakes for high
-     fake rates (b → j,  → e, etc.)
- 3) use explicit misidentification matrix for low fake rates:
-             j → b, , e, , 
-             b → e, , 
-  → e, , 
- 4) fit a set of correction factors to the data
- 5) run data/background comparisons in event counts
-    for each final state and KS test for distributions

## Slide 14

## Slide 15

## ♦ generate single isolated particles in central CDF detector

- ♦ generate single isolated particles in central CDF detector
- ♦ standard simulation and reconstruction
- ♦ study rates and Et dependence
- p( → )
- p( → )
- p(u → ) /p(g → )
- p(u → ) /p(g → )
- p(e+ → )*p( → e-)

- /105

- generated

- reconstructed

## Primary fake mechanisms:

- j → e
-       j  → q  →  0 →  → ee → e
- j → 
-       j  → q  →  + →   → 
- j → 
-       j  → q  →  + → 
- j → 
-         j  → q   → 0 →  → 
-     everything depends onj  → q  →  

## Slide 18

## ♦ correction categories:

- ♦ correction categories:
-     ○16 fake rates       ○  luminosity   ○ 4 efficiencies     ○  23 k-factors
- ♦ fit 44 parameters in all
-      to wide bins in , Pt for all final states
- ♦ introduced only as necessary, simple well, motivated
- ♦ constraints applied when available
**      **○ W NNLO x-sec, etc.
**      **○ CDF b-tag efficiency , etc.

## ♦Many can be 

- ♦Many can be
- identified with
- single final states
- ♦ 2 = 288/133+27

## ♦ simple, well-motivated

- ♦ simple, well-motivated
- ♦ consult with experts
- ♦ excess in e- final state
-  -observe excess in plug
-  -split p(j→e) into central and plug
-  -poor stats in plug –
-        add photon trigger
-  -fit returns a large 2
-  -tension between e-Met, ej and ejj
-  -further investigations revealed
-      central and plug trigger differences
-  -split central and plug trigger eff.

## agreement                  disagreement

- distorted due
- to low stats
- final states

## Slide 23

## Slide 24

## ♦ one photon and one tau

- ♦ one photon and one tau

## ♦ W high S/N, stats

- ♦ W high S/N, stats
- ♦ e j  fakes agreement
- ♦ e 4j Met top sample

## Good agreement in final states with no influence on fit

*Good agreement in final states with no influence on fit*

## ♦ 3j discrepancies significant,

- ♦ 3j discrepancies significant,
- but also difficult to rule out
- the mundane
- ♦ under investigation, NLO looks
- better but has not been completely
- explained so far

- ♦ the same discrepancy
- apparently affects the
- jet mass distributions

## ♦ intrinsic Kt is tuned 

- ♦ intrinsic Kt is tuned
-      on a few final states (ee, , ..)

- ♦ works well for majority
- ♦ does not work on
-      some final states

## ♦ “optimized” quasi-model-independent search

- ♦ “optimized” quasi-model-independent search

## Assumptions

_**Assumptions**_
- ♦ the new physics appears as *excess* on the SumPt tail
-     - high mass, threshold production
- ♦ the new physics appears mostly in one final state
-     - currently no method to combine final states
_**Limitations**_
- ♦ optimized for models matching assumptions well
- ♦ not sensitive to small mass peaks like Higgs
-        (a bump hunter is obvious addition)
- ♦ less sensitive to low-pt models

## Slide 32

## Vista

_**Vista**_

- Lesser generation equivalence
- e+ ≡ μ+
- Quark jets come in pairs
- 2j ≡ 3j
- 4j ≡ 5j
- bj ≡ bb ≡ bjj ≡ bbj

**Wbbjj**

_**Sleuth**_

**e+e-**

**e+pb3j**

******+********-**

******+pb3j**

**e-pb3j**

**e+p2b3j**

******-pb3j**

******+p2b3j**

**/**

**/**

**/**

**/**

**/**

**/**

**l+l-**

## ♦ for each event, 

- ♦ for each event,
- sum data and BG
- from data to infinity
- ♦ generate pseudo-
- experiments to access
- significance
- ♦ this defines P
- ♦ find combined global
- significance P
- ♦ “discovery” criteria
- is P<0.001

- ~

- ~

## ♦remove top, refit

- ♦remove top, refit
- ♦sleuth would “discover”
-      top with ~80pb-1
- ♦actual discovery was
-          about 60pb-1

## ♦top pseudo-experiments

- ♦top pseudo-experiments

## ♦ it would discover WW, even after refitting 

- ♦ it would discover WW, even after refitting
- ♦ reasonably sensitive to high mass Higgs
- ♦ not as sensitive to light Higgs as dedicated analysis
- ♦ less sensitive to single top than dedicated analysis

## Slide 38

## ♦ standard systematic error

- ♦ standard systematic error
- analysis would be ~ prohibitive
- and is not included
- ♦ discrepancies are debugged
- and model adjusted (physically,
- requiring agreement with all data)
- reducing systematic effects
- ♦ typical variation of a fit
- parameter gives 10% variation in
- SM background
- ♦ if we find limits on generic models,
- varying fit parameters slowly varies limits

*including systematics only makes a null result more null*

## the probability that the most discrepant final state would be more

-        discrepant  is 0.46 = no significant excess
*this does not prove no new physics *
- This global search reveals no significant indication of new physics
- There exists a simple, well-motivated implementation of the
-    standard model, consistent with the entire high-Pt dataset,
-    that also explains the SumPt tails of all final states

## the most discrepant final states:

## Slide 42

## The single most

*The single most*
*encompassing *
*test of the*
*Standard Model *
*on the energy *
*frontier to date*

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