Long lived particles searches

S. Tarem, S. Bressler, S. Vallecorsa, E. Kajomovitz, S. Trboush

A. Soffer, Nimrod

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)

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:1. Combined fit to beta using MDT/RPC/Tile - us2. 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

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

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

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

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

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

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

Two analyses

• GMSB Motivated– NLSP is reconstructed in ID and MS– MuGirl seeded 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– MuGirl seeded with EF -Muon Spectrometer Track– Weighted b average from MDT, RPC, Tile– ID agnostic, complementary to Tile+ID analysis

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 pdf from W/Z MC samples against data

• Smear signal samples

COMBINED

MS ONLYdataMC

dataMC

Background Estimation – b pdf

• Background content can be estimated directly from data if b pdf is momentum independent– Generate b pdf from muon candidates pT>30GeV– Convolute with momentum distribution in data

• Testing b pdf is consistent with control sample Z → mm– Small differences in calibration– Difference is systematic error

systematics

• Luminosity – currently 11%• Trigger efficiency• MC smearing

– bracket data between 2 smearing factors

– apply to signal MC

• Background estimation - b distribution of muons in data– b distribution does NOT depend

on the momentum– it DOES depend on h: shift b

distribution by it’s variation within h regions

MS onlyCombined

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