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Full Analysis Review for the Pixel-based R-hadrons search Oct 21 st 2015

Full Analysis Review for the Pixel-based R-hadrons search Oct 21 st 2015 Supp Note: ATL-COM-PHYS-2015-1310, <http://cds.cern.ch/record/2060790> B. Axen 3 , D. Barberis 2 , A. Favareto 2 , A. Gaudiello 2 , C. Gemme 1 , E. Guido 2 , B. Heinemann 3 , L. Jeanty 3 ,

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Full Analysis Review for the Pixel-based R-hadrons search Oct 21 st 2015

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  1. Full Analysis Review for the Pixel-based R-hadrons search Oct 21st 2015 Supp Note: ATL-COM-PHYS-2015-1310, <http://cds.cern.ch/record/2060790> B. Axen3, D. Barberis2, A. Favareto2, A. Gaudiello2, C. Gemme1, E. Guido2, B. Heinemann3, L. Jeanty3, S. Pagan Griso3, S. Passaggio1, L. Rossi1 1 Istituto Nazionale di Fisica Nucleare, Genova, Italy 2 University and Istituto Nazionale di Fisica Nucleare, Genova, Italy 3 LBNL, USA

  2. Introduction • Search for heavy (TeV scale), charged, long-lived, metastable (τ ~ tenths  tens ns)particles, by looking for anomalously large dE/dx in the ATLAS pixel detector • As we had problems in the metastable samples generation, the plan is to target the stable searches for the EOY, rather than use it as a safety check as initially assumed. • Cuts are optimized for the stable search, but should also be valid for the metastable cases. • This is a simple and well established search strategy • Simple: one dominant discriminating variable (dE/dx) one trigger (MET) • Well established: already employed both on 7 TeV (stable only search http://arxiv.org/abs/1211.1597), and on 8 TeV (stable and metastable, http://arxiv.org/abs/1506.05332) data.

  3. Motivations for an early analysis • We ran the walkthrough assuming: • a conservative estimate of the background scaling factor (~3) • the expected scaling of the signal production cross-sections • mass-dependent: the higher the mass of the heavy LLP, the higher the scaling factor on its production cross-section • e.g.: for pair production of 1.5 TeVgluinos, the production cross-section is boosted by a factor close to 50 • and some improvements done both on the detector (IBL) and the analysis (particle identification) • Expect to improve the sensitivity of our searches early in Run 2 • With 2 fb-1 we expect to be able to exclude R-hadrons (stable) w/ masses > 1350 GeV (compared to 1100-1200 in Run 1)

  4. Track dE/dx in Run2 • The track dE/dx is a truncated average of the dE/dx of the individual clusters associated to the track. • The largest releases are removed from to average to reduce the Landau tails. • In Run2 also the IBL clusters are used in the track dE/dx. • With respect to the pixel electronics, IBL has a rougher charge measurement. And in case of large charges (~ >1.5 MIPs), IBL saves the hit with an overflow flag, while the present electronics do not store the hit (if ~>8 MIPs). ATLAS-CONF-2011-016

  5. Track dE/dx in Run2 • Two main advantages in using IBL with respect to the Run1 definition: • The fraction of tracks that have a dE/dx computed with at least 2 clusters is significant larger: from 77% to 91% ! • Tails are significantly reduced. ~85% of tracks https://atlas.web.cern.ch/Atlas/GROUPSPHYSICS/PLOTS/PIX-2015-002/

  6. Mass estimate via the BB calibration • The mass is estimated via a Bethe-Bloch calibration function, tuned using low mass SM particles and a special dataset – Minimum Bias at 50 ns – in which the tracks are reconstructed to a low pT (100 MeV). Data and MC have different calibration parameters to properly account for dE/dx differences. proton mass vs bg

  7. However.. • On the track dE/dx the effect is diluted, especially when IBL is in overflow: • We apply a run by run correction based on a realignment of the dE/dx MPV. A possible systematic to cover the remaining differences is under study. • Moreover the effect for selected R-hadrons is minimal (80% of the R-hadron tracks have IBL in overflow) • The IBL electronics (and then the IBL dE/dx contribution) depends on radiation dose. This effect was unexpected and, as soon as it has beenobserved (the dE/dx drift has been ~-5% per 100 pb-1, lessening with integrated luminosity), the IBL has been regularly retuned when there is no beam. dE/dx mp

  8. Data samples and derivations • All data processed through SUSY6 Derivation: • Skimmed to include only muon and MET triggered events • Except for special studies (on MinBias and EGZ skims) • Content includes only basic information about reconstructed objects • Complete information about all InDetTrackParticles • Additional skimming applied to select events triggered by HLT_xe70 with at least one 50 GeV track for the primary analysis. • Blinded by removing the signal region definition

  9. R-Hadrons samples • Samples with pair produced gluinos are simulated in Pythia6 [2], with the CTEQ6L1 [3] partondistribution function (PDF) set, and with the AUET2B tune, incorporating dedicated hadronization routines [4,5] to produce final states containing R-hadrons. • Additionally gluinoMadGraph samples will be used to reweight events in order to properly reproduce the pTgg spectrum. MadGraph Pythia

  10. Selection: Event • Lower unprescaled MET trigger: EF_XE70 seeded by L1_50 • Good Run list: • PHYS_StandardGRL_All_Good • Offline confirmation of the missing energy  See SLIDE • MET >130 GeV • A primary vertex with at least 5 associated tracks. • Then we require that in the event there is at least one track fulfilling selection cuts • In case more than one track is found, the one with the highest pT is selected.

  11. Trigger and MET Trigger efficiency • Trigger • Efficiency on stable R-hadron signal independent of mass and around 32% • Offline MET cut • MET reconstructed from selected and calibrated AntiKt4EMTopo jets, muons, electrons, and soft track term • MET > 130 GeV: Good signal / background ratio and enough stat in BKG samples • Efficiency of offline MET requirement around 90% across range of stable R-hadron masses • Systematic uncertainties to be considered • Apply half of difference between trigger efficiency measured in Z->μμ data (calo MET) and W->μν (real MET) data and simulation as systematic uncertainty on signal efficiency • Investigate systematic uncertainties on signal efficiency due to offline MET cut Reconstructed MET after trigger selection in 1200 GeV R-hadron sample

  12. Selection: Track • Good quality primary track: • Implicit in the reconstruction Si hits>=7 (relaxed wrt to Run1 when there was a tight filter at Tier0 requesting 6 SCT hits  sensitivity to shorter tracks) • Inner most LayersexpectInnermostPixelLayerHit >= 1 && IBLHits >= 1 ) || (expectInnermostPixelLayerHit < 1 && expectNextToInnermostPixelLayerHit >= 1 && BLayerHits >= 1) • Enough hits to compute a good dE/dxnumberOfUsedHitsdEdx >= 2 • pT > 50 GeV • Relaxed with respect to Run1 (when there was a tight 80 GeV filter at Tier0) • Isolation • no other track with pT < 1 GeV in a cone of 0.02 in R around the track direction (was 0.25 in Run1). This cut is implemented mainly to guarantee that the reconstructed ionization is not spoiled by the proximity of other reconstructed tracks, while the isolation from jets is done at calo level. • Kinematic cuts • p>150 GeV && err_p < 0.5 • Transverse Mass > 130 GeV • ParticleIDVeto: !isMuonSignal && !isElectronJet && !isHadronJet • Ionization is > 1.8 MeV g-1 cm2, corrected for slight h-dependence

  13. Muons rejection • Muons are identified by the CP medium quality recommendations. • While this tagging is very efficient for real muons (97% evaluated on Z  uusample), it does not recognize as muons slow R-particles as the R-hadrons. • This is due to the request of a large number of precision hits in the MS. • Thanks to this feature, the muon veto – that in Run1 was applied only in the metastable search- is now applied also on the stable case. • This implies a loss of about 30% of stable signal sample (the fastest R-hadrons) but reduces the background of a factor of 2 [while in Run1 the fraction of identified muons in the stable sample was about 70%].

  14. Electron and Jet rejection • Associate tracks to jets if they are found within a cone of 0.05 DR • R-Hadrons should deposit little energy in the calorimeters: • Should have very low EJ/pTR (and this is what we see in MC) • Additionally don’t expect a large EM Fraction • Reject candidate tracks if they are associated to a jet which is identified as • Electron jets: if DR < 0.05 && EM Fraction > 0.95 • Hadron Jets: if DR < 0.05 && EJ/pTR >0.5

  15. Ionization h-dependent requirement • As the MPV of the released charge slightly depends on the track length, the dE/dx has a slight dependence on eta. • Evaluated with Bkg2: larger errors (due to lower available statistics) but behaviour compatible with Run1 • Actual ionization cut: dEdx>1.8 +p1|eta|+p2|eta|2+p3|eta|3

  16. Cut-flow data • Data period D and E  859 pb-1 • In Run1 in we found ~366 events/fb-1 before the ionization cut in the stable search. • This does not necessary translate in a factor 4 larger bkg in the mass range of interest (slide 24).

  17. Cut-flow signal • Expected signal events in 859 pb-1 • Slightly higher signal efficiency than in Run1

  18. Bkg estimation strategy • Wrt the note: • Updated to 859pb-1 • Bug fixed in mass spectra on data (affecting normalization) • We need (p,dE/dx) pairs in order to calculate the mass, but: • dE/dx has a not negligible dependence on eta • eta is kinematically dependent on p —> (p,eta,dE/dx) triplets • p and eta (kinematics) taken from a control sample with the same kinematics of the signal region (Bkg1) • dE/dx taken from a higher statistics sample, in order to better describe the tails, that are what we care most of (Bkg2) • The control samples are chosen in order to have high statistics, to be as signal free as possible • Random extraction from histograms: • random p • where is p? random eta, from the proper eta(p) histogram • where is eta? random dE/dx, from the proper dEdx(eta) histogram • Mass calculation: • mass spectrum either at the level before the ionization cut (what is now called ParticleIDVeto) • or after requiring dE/dx>1.8 + function(eta) • Normalization: • use pre-ionization spectra (bkg and data), count events with m<160 GeV (to be tuned?), normalize the bkg histogram • apply the normalization to the post-ionization bkg histogram

  19. Bkg1: same kinematics as the nominal region —> p, eta • Bkg2: higher statistics for better describing the dEdx tails • All the procedure (generation, normalization) is tested in a validation region (50<p<150 GeV), signal depleted, with kinematics different from the nominal region

  20. BKG1: p, eta distributions

  21. BKG2: dE/dx distributions

  22. VALIDATION REGION 1M events generated Normalization done on the distributions BEFORE the ionization cut (m<160 GeV): Distributions before/after the ionization cut:

  23. NOMINAL REGION 1M events generated Normalization done on the distributions BEFORE the ionization cut (m<160 GeV): Distribution(s) before/after the ionization cut:

  24. NOMINAL REGION wrt Run1 Distribution(s) after the ionization cut in Run1 and Run2: Background shape is stiffer in Run2 and bkg at large mass is significantly reduced.

  25. Systematics – Run 1 • What we did in Run1 and we think we should do for the analysis • Additional systematics will need to be studied to account for new muon, electron, and jet rejection cuts, as well as on new effects of dE/dx dependence on time.

  26. Estimating the sensitivity • Assumed luminosity: L = 2 fb-1 • For each one of a finite set of hypothetical nominal mass values (Mnom = 800  1800 GeV; 6 points, evenly spaced): • Define a counting mass window based on the fitted peak position and width for the corresponding signal MC sample distribution (after full selection) • Counting window: 1.4  around the peak • Keeps ~ 80% of the selected signal MC events • Count the properly scaled number of events from the data-driven background sample within the counting mass window: nBkgExp • The data-driven background sample is normalized to data (Period D+E: Lnorm= 859 pb-1) at low mass values (M < 160 GeV) • Scale this sample to the assumed luminosity L • Properly scale the statistical error on nBkgExpas well • Similarly, count the number of selected MC signal events within the counting mass window to estimate the signal efficiency  and, from it, (given NLL and L) the expected number of signal events: nSigExp

  27. Estimating the sensitivity • The 6 counting windows fully cover the reconstructed mass range Mreco 630  2650 GeV • The sensitivity of the search (95% CL median upper limit on the signal strength in a bkg-only hypothesis) is calculated, for each Mnom, by means of official ATLAS statistical tools: • a C++ implementation of the RooStats HistFactory tool to encapsulate the pertinent data (nSigExp, nBkgExp, with their errors) and a typical counting experiment statistical model in a RooWorkspace object and save it in a root file • the root macro ‘StandardFrequentistCLsDemo.C’ to evaluate the expected limit within the CLS prescription

  28. Estimating the sensitivity • A 9% systematic error on the assumed luminosity is taken into account • The systematic uncertainties on the signal and background yields are provisionally set at 20% and 10% level respectively, based on the order of magnitude of these errors as estimated in Run1 • All systematic errors are treated as gaussian-distributed nuisance parameters Assumed NLL and corresponding 95% CL expected upper limits on signal strength

  29. Estimating the sensitivity • An expected lower limit on the stable gluino R-hadron mass is derived by comparing the expected cross-section upper limits to the lower edge of the 1  band around the theoretically predicted cross-section Sensitivity @ L = 2 fb-1 M > 1350 GeV (95% CL) Was 1115 GeV for the stable in Run1 with 18 fb-1

  30. Corrections factor to be applied • MadGraph reweighing of gluino pT spectrum correction: • Computed , to be applied on signal • Trigger efficiency correction • Either correction or systematics • dE/dx MC/data difference; effect on ionization cut • Was a systematics, could even be a correction • Pile-up reweighting • Minor… • dE/dx correction due to IBL • Computed, applied on data • Mass correction: • Heavy particles are heavier than expected; 3% scale factor applied, could be smarter.

  31. Outlook EOY • Stable only analysis • Unblind the full 2015 statistics • Compute the main systematics uncertainties on data and simulation. • Apply the correction factors mentioned before Paper – Moriond 2016 • Analysis on the metastable RH and chargino samples. • Generation of samples completing the validation! • Compute all the systematics uncertainties accurately • Improve understanding and cure of IBL instability. • Alternative background methods.

  32. Bonus: Metastable sample • recently obtained the first satisfactory sample of metastable R-hadrons (simulation for all samples on-going) • Cut flow indicates that the current cut choices are already well tuned for Gluino_1000_qq_100_10ns Run2 Gluino_1000_qq_100_10ns Run1

  33. Spare C. Gemme, INFN Genova CONF-2010-109

  34. dE/dx more

  35. MPV_dEdx(eta) • From Bkg2: 6 bins of eta (chosen in order to have them ~equally populated) • Fit to the dEdx with a CB • MPV as a function of eta

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