# Presentation: 098720

## SleuthA quasi-model-independent new physics search strategy

- Bruce Knuteson
- Berkeley

- Motivation
- Strategy
- Algorithm
- Results

- 	ACAT 2000	Oct 17, 2000

## Sleuth

- Motivation

- Most searches follow a well-defined set of steps:
- 	• Select a model to be tested
- 	• Find a measurable prediction of the model differing as much as possible from the prediction of the Standard Model
- 	• Check those predictions against the data
- This approach becomes problematic if the number of competing candidate theories is large . . . and it is!
- Is it possible to perform some kind of “generic” search?
**Sleuth**

## Motivation

- The word “model” can connote varying degrees of generality
- 	- A special case of a class of models with definite parameters
- 			mSUGRA with M1/2=200, M0=220, tanβ=2, μ<0
- 	- A special case of a class of models with unspecified parameters
- 			mSUGRA
- 	- A class of models
- 			SUGRA
- 	- A more general class of models
- 			gravity-mediated supersymmetry
- 	- An even more general class of models
- 			supersymmetry
- 	- A set of even more general classes of models
- 			theories of electroweak symmetry breaking
- Most new physics searches have generality  11⁄2 on this scale
- We are shooting for a search strategy with a generality of  6 . . . .

- generality

## Sleuth

- Motivation

- Another, separate issue:
- How do we quantify the “interestingness” of a few strange events *a posteriori*?
- 		e.g. Barnett and Hall, PRL 77:3506 (1996)
- After all, the probability of seeing _exactly_ those events is zero!

**Sleuth**

- How excited should we be? How can we possibly perform an unbiased analysis after seeing the data?

- CDF

## Motivation

- Other advantages of **Sleuth**:
- 	Emphasizes an _understanding of the data_ (rather than what the data have to say about a particular model)
- 	Provides a systematic method for analyzing the entire data set (leaving no stone unturned!)
-   	Allows an approach that keeps attention focused on the most promising channels (rather than optimizing cuts for a signal that does not exist)
- 	Allows for surprises . . .

## Sleuth

- A new generic search algorithm

- Motivation
- Strategy
- Algorithm
- Results

- Final states
- Variables

## Strategy

- Initial thought:
- 	Consider inclusive final states, such as e  X
- However:
- 	- The presence of an extra object in an event often qualitatively changes the probable interpretation of the event
- 	- The presence of an extra object in an event generally changes the variables that one would want to use to characterize the event
- 	-  Allowing inclusive final states leaves an ambiguity in definition
- Therefore:
- 				We consider *exclusive* final states

- Final states

## Strategy

- More precisely:
- 	We assume the existence of standard object definitions
- 		These define e, μ, ****, ****, j, b, c, ET, W, and Z fi
- 	All events that contain the same numbers of each of these objects belong to the same final state

- e+

- e-

- Z

- j

****

- e+

- ET

- j

- j

- j

- e.g.,

- Final states

## Strategy

- 	Construct a set of variables for each possible final state
- However:
- 	- There are a lot of final states!
- 				eX alone comprises several final states
- 	- Our variables need to be robust
- 				Otherwise it will be too easy to change them
- 					after looking at the data!
- 	-  Our variables ought to be
- 				well-motivated (sensitive to new physics)
- 				simple and few
- Therefore:
- 	Instead of choosing a separate set of variables for every conceivable final state,  we construct a general rule
-  : (final state)  { variables }

- Variables

## Strategy

- 	What is it we’re looking for?
- 		The physics responsible for EWSB
- 	What do we know about it?
- 		 Its natural scale is a few hundred GeV
- 	What characteristics will such events have?
- 		 Final state objects with large transverse momentum
- 	What variables do we want to look at?
- 		 pT’s

- Variables

## (adjust slightly for idiosyncrasies of each experiment) 

- 	If the final state contains	Then consider the variable
- 	   1 or more lepton
- 	   1 or more /W/Z
- 	   1 or more jet
- 	   missing ET

- (adjust slightly for idiosyncrasies of each experiment)

- Strategy

- Variables

## Sleuth

- Motivation
- Strategy
- Algorithm
- Results

- A new generic search algorithm

## Algorithm

- Input:  1 data file, estimated backgrounds

- transform variables into the unit box
- define regions about sets of data points
- Voronoi diagrams
- define the “interestingness” of an arbitrary region
- the probability that the background within that region fluctuates up to or beyond the observed number of events
- search the data to find the most interesting region, 
- determine ****, the fraction of *hypothetical similar experiments* (hse’s) in which you would see something more interesting than 
- Take account of the fact that we have looked in many different places

- For each final state . . .

- Output: , ****

- Overview

## Algorithm

- We begin by applying a variable transformation that makes the background distribution uniform in the “unit box” — [0,1]d
- 	1.  Put the background events into the unit box

- original space

- unit box

- Projections along each axis are now uniform

- Variable transformation

## Algorithm

- 		[Iteratively switch pairings to minimize the maximum distance moved]

- Variable transformation

## Algorithm

- A quick example of how this might look for data:

- data

- background

- signal?

- Variable transformation

## Algorithm

- The transformation maps the signal region into the upper right-hand corner of the unit box

- The background data events are uniformly distributed, as desired,
- 	and the signal cluster is “obvious”

- Variable transformation

## Algorithm

- seven 1-regions

- one 2-region

- Regions

- An *N-region* (about a cluster of N data points) is the set of all values of x closer to a data point in that cluster than to any other data point in the sample.

## Search the space to find the region of greatest excess,  

- Search the space to find the region of greatest excess, 

- Algorithm

- Search

-  

-  . . . etc.

## Algorithm

- hse’s

- generate “data samples” from the background distributions
- Allow numbers of events from each background source to vary according to statistical and systematic errors
- find the most interesting region in each pseudo sample
- Use same searching algorithm as for the actual data
- compare the most interesting region in each pseudo sample with 
- Determine ****, the fraction of *hypothetical similar experiments* in which you see something more interesting than 

- Perform many hypothetical similar experiments

## Sleuth

- Motivation
- Strategy
- Algorithm
- Results

- A new generic search algorithm

## Results

- Sensitivity check: tt in eμX

- Excesses corresponding
- (presumably)
- to WW and tt

- DØ data

- Let the backgrounds include

- Excess corresponding
- (presumably)
- to tt

- DØ data

- No evidence for new physics

- DØ data

- fakes
- Z
- WW
- tt

- fakes
- Z
- WW
- tt

- fakes
- Z
- WW
- tt

- 1)

- 2)

- 3)

## Results

- Sensitivity check: tt in Wjjj(nj)

- 	All over-flows in last bin

- Could Sleuth have found tt in the lepton+jets channel?

- Sleuth finds ******** > 3 in 30% of an ensemble of mock experimental runs

- Monte Carlo

- DØ Data

- DØ preliminary

## Results

- Sensitivity check: Leptoquarks in eejj

- 	All over-flows in last bin

- We can run mock experiments with hypothetical signals, too
- What if our data contained leptoquarks?

- (Assume scalar,  = 1, mLQ = 170 GeV)

- Sherlock finds  > 3.5 in > 80% of the mock experiments

- (Remember that Sherlock “knows” nothing about leptoquarks!)

- DØ preliminary

## Results

- We have applied Sleuth to roughly half of these final states.

- DØ data

- analyzed with **Sleuth**

- analyzed in a spirit similar to **Sleuth**

## Results

- Results agree well with expectation
- No evidence of new physics is observed

- DØ data

- DØ preliminary

- DØ preliminary

## Results

- Combining many final states

- We can account for the fact that we have looked at many different final states by computing

- The correspondence between      and the minimum found for the final states that we have considered is shown here

- DØ preliminary

## Conclusions

**Sleuth** is a quasi-model-independent search strategy for new high pT physics
- Defines final states and variables
- Systematically searches for and quantifies regions of excess
- Allows an *a posteriori* analysis of interesting events** **
**Sleuth** appears sensitive to new physics
- But finds no evidence of new physics in DØ data
- Should be a useful data-driven search engine in Run II

- hep-ex/0006011