KET-BSM meeting Aachen, April 2006

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

2. The situation in 2006 • We still don’t know the origin of EW symmetry breaking • The Higgs boson is not discovered yet • Even with the SM Higgs: • ‘fine tuning’ is required in the model to remain valid to high energies?, • Gravity is not included?, Fermion masses? What is Dark Matter?,… •  typical solutions by increasing the number of • symmetries, dimensions, forces, … Higgs ? Something else? Sascha Caron page 1

3. The situation in 2006 Investigate if EW symmetry breaking is caused by a Higgs. Investigate if there is other physics beyond the Standard Model 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? Sascha Caron page 2

4. The situation in 2006 Worried about your search strategy? Number of models 5 30 Number of Higgs doublets hep-th 0411129 SUSY spectra from special string vacua Sascha Caron page 2b

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

6. MSSM The situation in 2006 CMSSM MN2SSM SUSY with extra Dim SUSY with extra forces SUSY+ little Higgs, … SUSY VERSIONSOF THE SM NMSSM (an additional Higgs singlet) 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’ modelis this parameter point?  Is it efficient to work like this?

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

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

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

11. Martin Wessels Ph.D. thesis RWTH Aachen 100000 Is this approach sensitive to New Physics? 1000 MC SM experiments with larger deviation 10 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 Sascha Caron page 9

12. Is this possible at LHC? Is this the best strategy for an ‘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. 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…?) Idea: “less model dependent” SUSY/DM searches Is this the best strategy for ‘early discovery’?

15. SM prediction (with complete uncertainty) in (finally) all channels (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) Later: A way to learn what we have found What do we need for this? Sascha Caron page 13

16. What can we learn 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 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, ….  General BSM Model Generators to determine the efficiency of such an approach for any model (can we be more general?)

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

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. A bit more motivation The SUSY search strategy • Examples of SUSY searches at LHC: • jjjjv channel cutsoptimized on specific CMSSM points • 1 jet with pT >100 GeV, 4 jets (pT>50 GeV) • ETMISS > max(100 GeV ,0.2Meff) • Transverse sfericity ST>0.2 • No isolated muon or electron (pT>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? Sascha Caron page ?

21. A significant danger is finding correlations and signals 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?  Unsolvable problem if you use 2000 PhD students Sascha Caron page 20

22. Quantify the deviations Step 1: Repeat the whole analysis with a pseudo data experiment (dice your own MC data) many times. Step 2: Count how many times you find deviations bigger than in those in your real data. 3% Number of channels 3% of the “Pseudo H1 experiments” have found a bigger deviation 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 Sascha Caron page 21

23. What are the numbers for ATLAS or CMS?