1. KET-BSM meeting Aachen, April 2006 View from the Schauinsland in Freiburg a couple of weeks ago View from the Schauinsland in Freiburg a couple of weeks ago Sascha Caron University of Freiburg How does nature behave at 1TeV ? A search strategy for SUSY et al. Outline: 1 st Motivation, 2 nd Strategy, 3 rd Questions

2. The situation in 2006 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 yet  The 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 of  typical solutions by increasing the number of symmetries, dimensions, forces, … symmetries, dimensions, forces, … Higgs ? Something else? Higgs ? Something else? Sascha Caron page 1

3. Investigate if there is other physics beyond the Standard Model Investigate if there is other physics beyond the Standard Model Investigate if EW symmetry breaking is caused by a Higgs. Investigate if EW symmetry breaking is caused by a Higgs. Part 1 Higgs working groups at ATLAS and CMS Part 1 Higgs working groups at ATLAS and CMS Part 2 This approach: Data mining strategies How to find anything potentially interesting and previously unexpected in the data? unexpected in the data? Part 2 This approach: Data mining strategies How to find anything potentially interesting and previously unexpected in the data? unexpected in the data? The situation in 2006 Sascha Caron page 2

4. The situation in 2006 Sascha Caron page 2b Number of Higgs doublets Number of models 305 Worried about your search strategy? hep-th 0411129 SUSY spectra from special string vacua

5. What do we expect to find at the LHC? The situation in 2006 One physicist's schematic view of particle physics in the 21st century (Courtesy of Hitoshi Murayama) (Courtesy of Hitoshi Murayama) Sascha Caron page 3

6. MSSM CMSSM SUSY VERSIONS OF THE SM NMSSM (an additional Higgs singlet) MN2SSM SUSY with extra Dim SUSY with extra forces SUSY+ little Higgs, … The situation in 2006 Choose this point, look at the LHC data, exclude or not! Sascha Caron page 4

7. 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’ model is this parameter point?  Is it efficient to work like this?

8. Examples: General Search for new Phenomena at H1 (2004) and D0 Sleuth approach (2002 but only top final states) Finding the unexpected – explaining the origin The other strategy: START FROM THE DATA 1)Search for deviations in (almost )all final states (they are all interesting either as signal or to understand background) 2)Determine ‘deviation(s)’ or ‘inconsistencies’ (e.g. all muon final states have problems) states have problems) 3) Determine their origin (detector effect, Monte Carlo?, new physics?) 4)Re-determine expectation and repeat step 1-4) until publication in journal repeat step 1-4) until publication in journal Sascha Caron page 7

9. Event yields for HERA 1 data First time a HEP experiment analyzed all final states Example: H1 General Search Sascha Caron page 8 Channels which have not been syst. studied before

10. We investigated all M all and ΣP T distributions Sascha Caron page 9 We developed a simple and powerful algorithm to find and quantify deviations automatically

11. Is this approach sensitive to New Physics? Sascha Caron page 9 H1 tested various models and found compatible sensitivity to direct searches in all of them (without tuning a cut)! Next step for me: Sensitivity tests of such an approach for CMSSM points at ATLAS Martin Wessels Ph.D. thesis RWTH Aachen MC SM experiments with larger deviation 10 100000 1000

12. Is this possible at LHC? Is this the best strategy for an ‘early discovery’? ‘early discovery’? What do we need for this search? What can we learn from theory?

13. Is this possible at LHC ? Yes ! (H1 has made the ‘proof of principle’) Sascha Caron page 11

14. Is this the best strategy for Is this the best strategy for ‘early discovery’? ‘early discovery’? Answer 1 : DEPENDS ON THE PHYSICS Answer 2 : I’M NOT 100% SURE TO BE HONEST We like to start from a ‘simpler’ scenario and to extend (after we know some of the detector response and of physics at 1 TeV) Our attempt : Start from channels where one might expect something new and you don’t know exactly what and where you can predict some of the background from data pT_miss channels (Dark Matter…?) pT_miss channels (Dark Matter…?) Idea: “less model dependent” SUSY/DM searches Idea: “less model dependent” SUSY/DM searches

15. What do we need for this? SM prediction (with complete uncertainty) in (finally) all channels (Multi purpose event generators) (Multi purpose event generators) A multi-purpose analysis framework (as in H1) (I thought it would be nice to run a simple version of this even on-line) Uncertainties and fudge factors from data (calibration with data candles, use data without pt_miss, use fits to fudge factors, use a global background determination strategy, make ‘fake data’ for each channel, use fast ways to go from 1-4) (calibration with data candles, use data without pt_miss, use fits to fudge factors, use a global background determination strategy, make ‘fake data’ for each channel, use fast ways to go from 1-4) Later: A way to learn what we have found Sascha Caron page 13

16. What can we learn we learn from theory? from theory? - What are ‘model independent’ the best variables to determine the underlying physics (Et, mass, endpoints?, spin information, something else?) - What do you need to determine the nature@1TeV Lagrangian? Do you know already how to do this?  Would it be helpful to publish a ‘pseudo’ ATLAS/CMS signal? - Tune QCD radiation: Best MC tunes via fits to almost all published data - How can we best use Jet+X events to determine Jet+Ptmiss+Y events? (e.g. include fit procedure into Generators to determine some QCD radiation weight factors instead of predicting e.g. W+jets events with Z+jet events?)

17. What can we learn we learn from theory? from theory? Attempts to determine LHC signals: - LHC olympics (a signal only ‘fun’ analysis) - LHC inverse problem - BARD (automizing ME calculations of Madgraph and fitting to signals)  Any interest from german theory to start something better? Determine a general LHC Standard Model: Madgraph/event, Sherpa/Amegic, …. Madgraph/event, Sherpa/Amegic, …. General BSM Model Generators to determine the efficiency of such an approach for any model (can we be more general?)

18. A General analysis of LHC data Theory and ‘Going the way into the other direction’…

19. Summary I’ve tried to illustrate what we like to do and why (build such a framework for ATLAS) Somebody interested in joining a general data analysis strategy in germany ?

20. The SUSY search strategy Examples of SUSY searches at LHC: jjjjv channel cuts optimized on specific CMSSM points 1 jet with p T >100 GeV, 4 jets (p T >50 GeV) E T MISS > max(100 GeV,0.2M eff ) Transverse sfericity S T >0.2 No isolated muon or electron (p T >20 GeV) better signal to background with a extra lepton + scanning on E_T distributions   I think we can gain sensitivity by exploring more channels (or by subdividing the data instead of cutting) Does the true signal slip through our harsh cuts? A bit more motivation Sascha Caron page ?

21. A significant danger is finding correlations and signals that do not really exist. that do not really exist. Many examples in particle physics history We are looking for deviations … How surprised should we be to find some? How likely is a 4-5 sigma deviation at LHC even if there is nothing in the data? Sascha Caron page 20  Unsolvable problem if you use 2000 PhD students

22. Step 1: Repeat the whole analysis with a pseudo data experiment (dice your own MC data) many times. Quantify the deviations 3% Sascha Caron page 21 Step 2: Count how many times you find deviations bigger than in those in your real data. 3% of the “Pseudo H1 experiments” have found a bigger deviation Number of channels 1 10 -1 10 -2 Probability to find deviation in this channel I know that this is not a new idea, but we do not often use it

23. What are the numbers for ATLAS or CMS?