day 4: measurements @ LHC Peter Wittich Cornell University
LHC measurements: what to expect first job: rediscover the Standard Model calibration, alignment early measurements late measurements start with overview of how a HEP analysis is done not intended to be complete - see e.g. TDR s for a lot more 2
interesting reads Experiments physics TDRs CMS: http://cmsdoc.cern.ch/cms/cpt/tdr/ benchmarks, from 2006 ATLAS: http://atlas.web.cern.ch/atlas/groups/ PHYSICS/TDR/TDR.html earlier (999), arguably more optimistic Theory paper: http://arxiv.org/pdf/hep-ph/ 050422v (gianotti &mangano), has info on calibration strategies at high level of abstraction read these for look for model x with lumi y 3
how analysis is done search vs measurement final state to consider test statistic counting, shape, SM bkgnds? studies with toy Monte Carlo choose data sets, trigger apply basic preselection bkgnd estimate MC and data driven test bkgnd estimate control regions modeling of reco, trigger, underlying physics test statistic iterate on selection until control regions are ok shapes are ok S/N is optimal many repetitions here MC data sets >> signal blindness have not yet looked at signal data collaboration approval to open the box discovery or limit significance cannot simply re-run whole analysis with more data; need to repeat process 4
how analysis is done search vs measurement final state to consider test statistic counting, shape, SM bkgnds? studies with toy Monte Carlo choose data sets, trigger apply basic preselection bkgnd estimate MC and data driven test bkgnd estimate control regions modeling of reco, trigger, underlying physics test statistic iterate on selection until control regions are ok shapes are ok S/N is optimal many repetitions here MC data sets >> signal this is where pheno stops 4 (by necessity) blindness have not yet looked at signal data collaboration approval to open the box discovery or limit significance cannot simply re-run whole analysis with more data; need to repeat process
how analysis is done search vs measurement final state to consider this is where most of the test statistic counting, work shape, is... SM bkgnds? studies with toy Monte Carlo choose data sets, trigger apply basic preselection bkgnd estimate MC and data driven test bkgnd estimate control regions modeling of reco, trigger, underlying physics test statistic iterate on selection until control regions are ok shapes are ok S/N is optimal many repetitions here MC data sets >> signal blindness have not yet looked at signal data collaboration approval to open the box discovery or limit significance cannot simply re-run whole analysis with more data; need to repeat process 4
Role of Simulation many differential background estimates accomplished via Monte Carlo calculations Steps Generate Simulate Reconstruct Generate: Hard process, hadronization output: four-vector of final state particles ( generator-level ) Simulate: interaction of particles with detector output: hits in the detector (additional radiation) Reconstruct: hits recon objects output: e cands, jet cands, μ cands, ( full reconstruction ) Generators: Pythia, MadGraph, ALPGEN, Herwig, Simulation tools: EGS (γ), GEANT (material), generator-level as best possible; doesn t include extra radiation in the detector though 5
data-driven backgrounds certain backgrounds cannot be estimated via simulation statistics/process impossible to replicate (QCD e.g.) depend on tails of tails - don t trust simulations data-driven estimate : extract estimate from data trivial example: bifurcated analysis find two variables, x, y, that are uncorrelated signal region: A; others: signal-free & dominated by bkgnd y B D NA = NC/ND x NB extrapolate into signal region absolute value from data A C x QCD, MET tails, as possible sources (Met vs Iso Run popular) 6
From Tuesday: data analysis trigger s goal: collect data. 5 Petabytes/year. how to analyze? tiered computing model worldwide access to computing resources the grid http://lcg.web.cern.ch/lcg/ Arguably largest computing grid on the planet. 7
Rediscover the Standard Model wboson allheldata cmup_cem_mt zboson allheldata cmup_cmup_dimass 7000 6000 CDF Data Underflow: 397 Overflow: 69 300 CDF 5000 250 4000 3000 Data mc_ztop0i_mt mc_ztop4i_mt mc_wtop2i_mt mc_wtop3i_mt 200 50 Data mc_ttopel_dimass mc_sexo8t_dimass mc_wtopw_dimass mc_ztopcz_dimass mc_ztopcz_dimass 2000 0 00 50 0 20 40 60 80 0 20 40 0 20 40 60 80 0 20 Wed Dec 2 3:45:5 2005 Tue Dec 20 5:25: 2005 first measurements might be charged track multiplicity at s=4 TeV early work will be using W,Z bosons as standard candles top as standard candle for hadronic activity, b tagging 8
standard candles A standard candle is an astronomical object that has a known luminosity. (wikipedia.org) calibration sources where we assume PDG properties test calibrations and simulation viz. these properties drell yan resonances: J/psi, upsilon, Z ee, μμ (ττ?) ECAL energy scale, muon momentum scale W eν, μν MET resolution di-jet, γ-jet balancing hadronic energy scale top quark pairs hadronic activity, b tagging useful standard candles: large σ, straightforward selection 9
calibration and alignment calibration topics electromagnetic energy scale muon momentum scale hadronic energy scale ignore electronics calibrations here alignment topics tracker alignment calorimeter alignment muon system alignment material map B field map
electromagnetic mass scale basic scale evaluated in test beam with known energies ATLAS +20% material cosmic rays to intercalibrate input best estimate of material; test with Z ee uniformity in η, Φ extrapolate to other energy scales
momentum ATLAS scale p Meas j = K iα i B i s j + δx i + λ i ɛ i (p j ) best estimate of momentum depends on accurate material description good knowledge of B field alignment applies to inner detector and muon systems study with Z decays, extract Bi, delta xi, elossi 2 independent alignment <pb -.
hadron energy scale g + q q + γ q + q g + γ beforehand: test beam measurement pion beam of known energy in situ: dijet balancing: relative response per tower pion response in data (tracker) γ jet balancing: EM vs HAD response hadronic W s in top pair production 3
absolute had. energy scale.offset: removal of pile-up and residual electronic noise. 2.Relative (η): variations in jet response with η relative to control region. 3.Absolute (p T ): correction to particle level versus jet p T in control region. 4.Flavor: correction to particle level for different types of jet (b, tau, etc.) 5.Underlying Event: luminosity independent spectator energy in jet removed. 6.Parton: correction to parton level. semi-independent corrections, multiple stages examples (2) (3) Relative Response Before Absolute Response After After Before 2 3 Jet η 4 0 00 Jet p T (GeV) 4
data samples for JES dijet balancing 2 2 processes, QCD γ-jet diagrams correct response such that pt of event is balanced γ jet balancing beat EM calorimeter against hadronic calorimeter Z+jets momentum balance Statistical accuracy with fb - is <% for E T γ<800 GeV 5
Trigger object Muon JetMET L Seed OR of L prescaled bits L_SingleMu7 L_DoubleMu3 HLT thresh [GeV] None Iso Double 3 60 L Rate [khz] (Prescl) 0.003(4E3).9 -ii- -ii- 0.32 Total Prescale L_SingleJet30 0 0.02 (E4) L_SingleJet70 50 0.03 (0) L_SingleJet0 200 0.59 L_SingleJet50 50 0.06 L_Sgl50, Dbl70 85 L_Sgl50, Dbl70, Tpl50 CMS E32 60 Trigger Menu L_Sgl50, Dbl70, Tpl50, Qpl30 65 E3 E4 HLT Rate [Hz] 2 8 22 2 2 4 9 7 4 5 Electron Photon L_ETM45 L_SingleIsoEG2 L_SingleIEG5 L_DoubleIsoEG8 L_DoubleEG L_SingleIsoEG2 L_SingleEG5 L_DoubleIsoEG8 L_DoubleEG SumET RapidityGap 5 8 2 30 40 20 20 MinBias 6 2 0.53 4.0 2.5 0.74 0.50 4.0 2.5 0.74 0.50 0 7 2 7 0.2 0.8 8 3 0.6.8
easy vs hard measurements easy objects, hard objects to determine muons easier than electrons easier than taus existence of calibration samples Z ee, μ μ, but high-mass γ s? τ s? understanding triggers single object triggers easier than multi-object MET as ultimate multi-object trigger self-calibrating vs absolute measurements the less you need to know, the better gross distortions vs subtle changes types of searches counting experiment, shape 7
C. Campagnari, TASI 07 ScoreCard on objects Object Notes Typical eff Jet fake rate e μ Excellent resolution Improves with E Excellent resolution Degrades with E ~ 90% ~ -4 - -5 ~ 90% ~ -4 - -5 τ So-so resolution ~ 50% ~ -2 - -3 γ Excellent resolution Improves with E ~ 90% ~ -3 - -4 Jet Poor resolution (> %) ~ 0% if above threshold - Btag ~ 50% ~ % MET Depends on everything else in the event! 8
easy vs hard measurements easy objects, hard objects to determine muons easier than electrons easier than taus existence of calibration samples Z ee, μ μ, but high-mass γ s? τ s? understanding triggers single object triggers easier than multi-object MET as ultimate multi-object trigger self-calibrating vs absolute measurements the less you need to know, the better gross distortions vs subtle changes types of searches counting experiment, shape 9
Early searches self-calibrating searches will be done first require simple triggers: single object triggers do not require an absolute normalization of backgrounds an absolute understanding of energy scale or efficiencies examples: bump hunts ratios (e.g., dijet studies) 20
Example bump hunt: CMS μμ 0/pb - Events/50 GeV/0. fb 8 6 4 generator full reco CMS PTDR VII, CERN-LHCC-2006-02 2 400 0 600 800 00 200 400 60 µ + µ - mass (GeV) pp Z μμ, mz > TeV small SM backgrounds, invariant mass peak size depends on coupling strength and mass effect of day detector alignment knowledge μ - easy to do; need good μ mass scale beware the statistical implications 2 - easy to create signal
Example bump hunt: CMS μμ 0/pb - Events/50 GeV/0. fb 8 6 4 generator - Events/50 GeV/0. fb 8 6 4 full reco CMS PTDR VII, CERN-LHCC-2006-02 2 2 400 0 600 800 00 200 400 60 µ + µ - mass (GeV) 400 0 600 800 00 200 400 60 µ + µ - mass (GeV) pp Z μμ, mz > TeV small SM backgrounds, invariant mass peak size depends on coupling strength and mass effect of day detector alignment knowledge μ - easy to do; need good μ mass scale beware the statistical implications 2 - easy to create signal
interpretation of bumps Look in enough places and you ll see a bump; remember your statistics CDF Run II Preliminary ) 2 Events/( GeV/c 5 4 3 2 - ) 2 Events/( GeV/c 20 0 80 60 40 20 L = 2.5 fb data 40 - Drell-Yan QCD Other SM 60 80 200 220 240 260 280 300 320 340 2 M(ee) (GeV/c ) Probability of the Background Fluctuating to! N obs Prob of fluctuation! - -2-3 -4 CDF Run II Preliminary Expected Range for Min. Obs. Prob. N obs -2-3 -5 " L dt =.3 fb - 3 # evidence level -4 0 200 300 400 500 600 700 800 900 00 M(ee) (GeV/c 2 ) 22-6 50 200 250 300 350 400 450 500 550 Di-Electron Mass (GeV/c 2 )
example: counting experiment: low-mass susy counting experiment: expect n events (SM), observe m. are n and m compatible? LM : m g 600 GeV m q 550 GeV benchmark channels have large cross sections for low masses (strong production); σ~50pb (LM) hadronic decays have large BR, large acceptance CMS physics TDR, /fb Signal ttbar Z νν +j W/Z+j, dibos.+j single t QCD 6300 54 48 33 3 7 23
Example SUSY event jets + MET in CMS, ET=340,40,60, MET =360 24
Low-mass susy large cross sections suggest early discovery in small data samples require good understanding of MET triggers data-driven background estimates (Z νν, QCD) modern W, Z + n j MC estimates (Tilman talks) /fb probably not unrealistic but naive estimates (</pb) more challenging sheer size of excess helps scaling of statistical error is n 25
searches SUSY, extra-dimensions, LED, many theoretical models to solve same probs phenomenology of models very similar, many free parameters what s an experimentalist to do? SUSY example: long time experimental community stuck with msugra, though theory community doesn t believe it 40 5 2 free parameters our answer: model-independent searches 26
model-independent/signature searches develop a robust signature that is sensitive to a wide range of new physics models eg large MET, events with photons and MET, like-sign dileptons robust = well-understood SM background don t tune to any particular model no optimization for S/N: depends on S essentially a null-hypothesis test on the standard model strength: open to many possibilities weakness: less sensitive to any particular model favorite party game: would you find top in open search? both will be done at the LHC http://www-cdf.fnal.gov/physics/exotic/r2a/20080228.vista_sleuth/publicpage.html 27
theoretical help most useful help from theory community comes from the background studies too many new physics models already to effectively deal with accurate prediction of higher-order MC s are most useful understanding of PDF s also much appreciated a lot of work has gone on in last years in both cases experimental community is taking advantage of this 28
we find something - what then? once we have new physics: what is it? very challenging problem one we hope to have want to measure spectrum of new particles want to measure quantum numbers of new particles charges, masses, spins, coupling strengths much of this is very hard at the LHC compare top charge measurements at Tevatron motivation for ILC/CLIC -2Ln(L) -640-645 -650-655 -660-665 -670-675 -680 - CDF Run II preliminary L=.5 fb f+=0.87 - -0.5 0 0.5.5 2 fraction of ttbar events with q=2/3 f+ 29
summarizing tried to give you a flavor of how detectors work and why they are designed as they are how data is collected and analyzed and what is required to extract 4-vectors what sorts of things are easy to do and what s hard what you might expect from us early in LHC running this is an exciting time for HEP - your timing is exquisite! 30