RPVLL Searches Within SUSY: Strategies and Analyses

1. Long lived particles searches S. Tarem, S. Bressler, S. Vallecorsa, E. Kajomovitz, S. Trboush A. Soffer, Nimrod

2. What is RPVLL? • We work within the RPVLL subgroup of SUSY • It includes everything that does not rely on MET • Searches that require dedicated reconstruction • Searches that require special simulation • Most searches identify the decay of new heavy particles into known stable particles. • The search is then performed with a kinematic analysis of events containing those known particles. • A search for a long-lived particles requires dedicated trigger and reconstruction strategies • Other analyses at RPVLL: stopped gluinos, kinked tracks, non-pointing photons, highly charged particles (but some more w/o weird signatures)

3. Very long lived particles • There are 4 detector technologies being used to reconstruct SMP mass from it’s velocity or energy deposition. • Muon Spectrometer – RPC and MDT timing • TileCal timing + later dedx (possibly also Lar) • TRT timing and dedx • Pixel dedx • Combining / comparing the beta measurements of the different technologies can improve overall resolution • Two methods will be performed: • Combined fit to beta using MDT/RPC/Tile - us • Combining the separate mass results of pixel and tile – not us • Both approaches include checking consistency between measurements • The TRT is being used to cross check, and in specific beta ranges

4. First things to look for – 40 pb-1 • We first look for models that have large cross-sections • The first of these is Split-SUSY, where squarks are so much heavier than gluinos that only gluinos are produced • The gluino must decay via suppressed squark loops and may be stable • Yael: There is no “natural” model with stable gluinos • Us: If it’s found it will become natural – if not maybe we won’t be as sorry • Gluino hadronization into R-Hadrons is model dependent and it may also flip charge in the calorimeter • Hard to know what fraction will be charged in the ID or in the MS so our efficiency is model dependent

5. First things to look for – 40 pb-1 • Next is GMSB stau • The masses are much lower, and the velocity is higher • Both mass and velocity are closer to where the background is • They don’t flip anything – if they are stable they are like heavy muons

6. First Atlas paper draft on R-Hadrons- not ours • Search for R-Hadrons using ToF to the Tile calorimeter and dedx in the pixel detector • Obviously a charged track in the ID is needed • Require both tile and pixel to measure – pixel beta<0.87 • Require both to give a mass > 100 GeV

7. Tile+Pixel • After requiring a valid mass in both Tile and Pixel, 54.05 candidates are expected, and 48 are observed • Actual background estimate is from data – following our method • This result is now in an editorial board

8. What about us? • MDT and RPC took some time to calibrate their timing – we are behind – working on a conf-note for winter conferences • Calibration is still far from MC expectations

9. MDT Chamber t0 / RPC hit time distributions • MDT “calibrated” by chamber t0 • Shift data by mean • Time error estimated by sigma • Smear MC by sigma • RPC shifted by station • Exclude outer station y>0 hits; poor calibration

10. Two analyses • GMSB Motivated • NLSP is reconstructed in ID and MS • MuGirlseeded with ID track • Combined b fit from MDTT, RPC, Tile • Often includes more than one muon candidate • R-Hadron Motivated • NLSP track in MS only • MuGirlseeded with EF -Muon Spectrometer Track • Weighted b average from MDT, RPC, Tile • ID agnostic, complementary to Tile+ID analysis

11. Signal resolution • Resolution in MC is better than in data • Detector is not perfectly calibrated • Reproduce current calibration conditions in MC by smearing hit times • Test by comparing β pdffrom W/Z MC samples against data • Smear signal samples data MC COMBINED data MC MS ONLY

12. Background Estimation – bpdf • Background content can be estimated directly from data if bpdf is momentum independent • Generate bpdf from muon candidates pT>30GeV • Convolute with momentum distribution in data • Testing bpdf is consistent with control sample Z → mm • Small differences in calibration • Difference is systematic error

13. systematics • Luminosity – currently 11% • Trigger efficiency • MC smearing • bracket data between 2 smearing factors • apply to signal MC • Background estimation - bdistribution of muons in data • bdistribution does NOT depend on the momentum • it DOES depend on h: shift bdistribution by it’s variation within hregions MS only Combined

14. Our results • We expect a limit on R-Hadrons similar or slightly worse that the tile+pixel note • Depending on the model – for some it would be better • In the base model 50% of candidates are charged in the ID, only ~30% are charged in the MS – so we start from a lower efficiency • We can put limits on GMSB stau – more sensitive to relatively high beta • Not quoting limits now because we’ve improved … GMSB R-Hadrons