ket-bsm meeting aachen, april 2006 view from the schauinsland in freiburg a couple of weeks ago view...
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KET-BSM meeting Aachen, April 2006KET-BSM meeting Aachen, April 2006
View from the Schauinsland in Freiburg a couple of weeks agoView from the Schauinsland in Freiburg a couple of weeks ago
Sascha Caron
University of Freiburg
How does nature behave at How does nature behave at 1TeV ?1TeV ?
A search strategy for SUSY et al. A search strategy for SUSY et al.
(Note that this is a short and simple talk(Note that this is a short and simple talk) )
The situation in 2005The situation in 2005
• We still don’t know the origin of EW symmetry breaking We still don’t know the origin of EW symmetry breaking The Higgs boson is not discovered yetThe Higgs boson is not discovered yet
• Even with the SM Higgs: Even with the SM Higgs: ‘‘fine tuning’ is required in the model to remain valid to high energies?,fine tuning’ is required in the model to remain valid to high energies?, Gravity is not included?, Fermion masses? What is Dark Matter?,…Gravity is not included?, Fermion masses? What is Dark Matter?,…
typical solutions by increasing the number oftypical solutions by increasing the number of symmetries, dimensions, forces, …symmetries, dimensions, forces, …
Higgs ? Something else?Higgs ? Something else?
Sascha Caron page 1Sascha Caron page 1
Investigate if there is Investigate if there is other physics beyond the other physics beyond the Standard ModelStandard Model
Investigate if there is Investigate if there is other physics beyond the other physics beyond the Standard ModelStandard Model
Investigate if EW Investigate if EW symmetry breaking symmetry breaking is caused by the Higgs.is caused by the Higgs.
Investigate if EW Investigate if EW symmetry breaking symmetry breaking is caused by the Higgs.is caused by the Higgs.
Part 1Part 1Higgs working groupsHiggs working groupsat ATLAS and CMSat ATLAS and CMS
Part 1Part 1Higgs working groupsHiggs working groupsat ATLAS and CMSat ATLAS and CMS
Part 2Part 2This approach:This approach:
Data mining strategies Data mining strategies How to find something potentially How to find something potentially
interesting and previously interesting and previously unexpected in the data?unexpected in the data?
Part 2Part 2This approach:This approach:
Data mining strategies Data mining strategies How to find something potentially How to find something potentially
interesting and previously interesting and previously unexpected in the data?unexpected in the data?
The situation in 2005The situation in 2005
Sascha Caron page 2Sascha Caron page 2
What do we expect to find at the LHC?What do we expect to find at the LHC?
The situation in 2005The situation in 2005
One physicist's schematic view of particle physics in the 21st centuryOne physicist's schematic view of particle physics in the 21st century (Courtesy of Hitoshi Murayama)(Courtesy of Hitoshi Murayama)Sascha Caron page 17Sascha Caron page 17
MSSM
CMSSM
SUSY VERSIONSOF THE SM
NMSSM(an additional Higgssinglet)
MN2SSM
SUSY with extra DimOr SUSY with extra forcesOr ….
The situation in 2005The situation in 2005
Choose this point,Choose this point,look at the LHC data,look at the LHC data,
exclude or not!exclude or not!
Sascha Caron page 18Sascha Caron page 18
We found no deviation We have excluded this point/area which
is epsilon of the parameter space
We found a deviation
Does this mean that the ‘real’ modelis this parameter point?
Is it efficient to work like this?Is it efficient to work like this?
Examples:Examples:General Search for new Phenomena at H1 (2004) andGeneral Search for new Phenomena at H1 (2004) and
D0 Sleuth analysis (2002 but only top final states)D0 Sleuth analysis (2002 but only top final states)
Finding the unexpected – explaining the originFinding the unexpected – explaining the origin
New Strategy: START FROM THE DATANew Strategy: START FROM THE DATA
1)1) Search for deviations in Search for deviations in allall final states final states(they are all interesting either as signal or to understand background)(they are all interesting either as signal or to understand background)
2)2) Determine ‘deviation(s)’ or ‘inconsistencies’ (e.g. all muon finalDetermine ‘deviation(s)’ or ‘inconsistencies’ (e.g. all muon final states have problems)states have problems)3) Determine their origin (detector effect, etc.)3) Determine their origin (detector effect, etc.)
4)4) Re-determine expectation and Re-determine expectation and Go to step 1) until publication in refereed journal Go to step 1) until publication in refereed journal
Sascha Caron page 19Sascha Caron page 19
(btw. it would be nice(btw. it would be niceto speed up steps 1-4)to speed up steps 1-4)
• Event yields for HERA 1 data (all channels with SM exp. > 0.01 event)
• Good agreement for (almost) all channels
Example: H1 General SearchExample: H1 General Search
Sascha Caron page 19Sascha Caron page 19
Channels which have Channels which have not been syst. studied beforenot been syst. studied before
We investigatedAll Mall and ΣPT
distributions
Sascha Caron page 22Sascha Caron page 22
We developed We developed a simple algorithma simple algorithmto find and quantifyto find and quantify
deviations deviations automaticallyautomatically
General SearchGeneral Search
Sascha Caron page 19Sascha Caron page 19
I spend some time at the New Phenomena web pages at LHCI spend some time at the New Phenomena web pages at LHCexperimentsexperiments
A count of final states planned to be studied leads to A count of final states planned to be studied leads to 100-500100-500
However consider permutations of j,b,e,However consider permutations of j,b,e,µ,µ,ττ,v,,v,γγ, + consider e.g. charge?, + consider e.g. charge?
Up to 8 particle final states lead to about Up to 8 particle final states lead to about 4000040000
Did you have events with 2 photons , a jet and a muon at your LEP exp.?Did you have events with 2 photons , a jet and a muon at your LEP exp.?
General SearchGeneral Search
Sascha Caron page 19Sascha Caron page 19
I spend some time at the New Phenomena web pages at LHCI spend some time at the New Phenomena web pages at LHCexperimentsexperiments
A count of final states planned to be studied leads to A count of final states planned to be studied leads to 100-500100-500
However consider permutations of j,b,e,However consider permutations of j,b,e,µ,µ,ττ,v,,v,γγ, + consider e.g. charge?, + consider e.g. charge?
Up to 8 particle final states lead to about Up to 8 particle final states lead to about 4000040000
Yes I know we do not want to start with 40000 finalYes I know we do not want to start with 40000 finalstates at ATLAS states at ATLAS
What is the strategy?What is the strategy?
Is this possible at LHC? Is this possible at LHC?
Is this the best strategy for Is this the best strategy for
‘ ‘early discovery’?early discovery’?
What do we need for this search?What do we need for this search?
What can we learn from theory?What can we learn from theory?
Is this possible at LHC ? Is this possible at LHC ?
Yes ! H1 has made the Yes ! H1 has made the ‘proof of principle’‘proof of principle’
Is this the best strategy for Is this the best strategy for ‘ ‘early discovery’?early discovery’?
I’m not sure to be honest.I’m not sure to be honest.We (Freiburg) start from a ‘simpler’ We (Freiburg) start from a ‘simpler’
scenario and extend (after we know scenario and extend (after we know some of the physics at 1 TeV)some of the physics at 1 TeV)
Attempt : Start from channels where you Attempt : Start from channels where you expect something new but you don’t expect something new but you don’t know what exactlyknow what exactly
pT_miss channels (Dark Matter…?)pT_miss channels (Dark Matter…?) Idea: less model dependent SUSY Idea: less model dependent SUSY
searchessearches
What do we need for this search?What do we need for this search?
- Theory (with uncertainty) in all channelsTheory (with uncertainty) in all channels (Multi purpose event generators)(Multi purpose event generators)
- Uncertainties and fudge factors from dataUncertainties and fudge factors from data (calibration with candles, use data without (calibration with candles, use data without
pt_miss, use fits to fudge factors, use a global pt_miss, use fits to fudge factors, use a global strategy, make ‘fake data’ for each channel, use strategy, make ‘fake data’ for each channel, use fast ways to go from 1-4)fast ways to go from 1-4)
- Later: A way to learn what we haveLater: A way to learn what we have (LHC olympics, QUAERO, Bard) (LHC olympics, QUAERO, Bard)
People interested to join such an effort in GermanyPeople interested to join such an effort in Germany
An General analysis of LHC dataAn General analysis of LHC data
Theory and ‘Going the way into the other direction’…Theory and ‘Going the way into the other direction’…
(Yes this is known as the ‘inverse problem’ now, but my transperancy is older(Yes this is known as the ‘inverse problem’ now, but my transperancy is older))
What can we learn from theory?What can we learn from theory?
What are ‘model independent’ the What are ‘model independent’ the best variables to measure best variables to measure
(Et, mass, something else?)(Et, mass, something else?)
What do you need to determine What do you need to determine nature@1TeV? nature@1TeV?
Is their an interest to work Is their an interest to work togethertogether
(background, signal determination) (background, signal determination)
A significant danger is finding correlations and signalsA significant danger is finding correlations and signals that do not really exist. that do not really exist.
Many examples in particle physics historyMany examples in particle physics history
We are looking for deviations …We are looking for deviations …How surprised should we be to find some?How surprised should we be to find some?
How likely is a 4-5 sigma deviation at LHCHow likely is a 4-5 sigma deviation at LHCeven if there is nothing in the data?even if there is nothing in the data?
Sascha Caron page 24Sascha Caron page 24 Unsolvable problem if you use 2000 PhD studentsUnsolvable problem if you use 2000 PhD students
Step 1: Repeat the whole analysis with a pseudo data experiment(dice your own MC data) many times.
Quantify the deviationsQuantify the deviations
3%3%
Sascha Caron page 25Sascha Caron page 25
Step 2: Count how manytimes you find deviations bigger than in those in your real data.
3%3% of the of the ““Pseudo H1 experiments”Pseudo H1 experiments”
have found have found a bigger deviation a bigger deviation
Num
ber
of c
hann
els
Num
ber
of c
hann
els
1 101 10-1-1 10 10-2-2 ProbabilityProbabilityto find to find
deviation indeviation inthis channelthis channel
I know that this is not a new idea, but we do not often use itI know that this is not a new idea, but we do not often use it