New physics at the LHC

Transcription

1 New physics at the LHC Giacomo Polesello INFN Sezione di Pavia

2 Outline of lectures Prerequisites Why we believe that there should be physics BSM Proving that new physics is indeed there Detector understanding Measuring SM physics at 7-8 TeV Conclusions from not finding a new signal Searches for new physics General strategy Exploring in detail a comprehensive model (SUSY) Signatures from different models

3 Why we believe that there should be new physics and the LHC can help finding it

4 Motivations Observations unexplained by SM Dark matter problem Matter-antimatter asymmetry problem Fine-tuning problems Hierarchy problem associated with Higgs Flavour problem Strong CP problem Why so puzzles Charge quantisation Gauge coupling unification Proton stability Fermion mass hierarchy Why three generations

5 First evidence from rotation curve of galaxies. If mass concentrated in core (as observed from light emission) Orbital speed of stars decreases with the square root of the radius of the orbit

6 Amount of Dark matter in the universe Extremely precise results on Dark Matter abundance from measurement of anisotropies in Cosmic Microwave Background (CMB) If Dark Matter is made of Weakly Interacting Massive Particles (WIMP), what we observe is the relic abundance of these particles after the cooling of the universe

7 The WIMP miracle

8 The naturalness problem Key assumption: SM is Effective Field Theory valid up to scale >> TeV Radiative corrections to Higgs mass: +smaller Yukawa If =5 TeV already need cancellation between tree level and radiative corrections of 2 orders of magnitude

9 Proposed solutions New weakly coupled physics at TeV scale (SUSY) see tomorrow Higgs is composite: new strongly coupled physics at the TeV scale (Technicolor/composite theories) Extra Dimensions (ED) ~1TeV in (4+n) dimensions, gravity is weak because we observe its effects on a 4-d slice quantum gravity effects at the TeV scale Anthropic solution: statistical explanation of MPl >> mw: due to large number of input ensembles

10 Role of the LHC A 125 GeV scalar has been observed with properties consistent with the SM Higgs We need to understand why it is so light All proposed explanations imply some new physics to appear at the TeV scale: e.g. SUSY: a complete spectrum of new particles ED Kaluza-Klein resonances of SM particles Composite models We need to explore the TeV scale to solve the problem

11 Probing the TeV region in pp Collisions 11

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13 How it is done ns 13

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16 The Etmiss variable 16

17 Etmiss example (CMS) 17

18 Invariant mass/transverse mass New physics searches: look for kinematic structures in data Easy case: particle from new physics Decays into two visible particles: Peak in invariant mass of Two objects: m(ll), m(jj), m(jl), m(tt), m(zz) More difficult: new physics particle decays Into a visible and an invisible particle Transverse mass variable For the decay A->BC with B massless and C invisible 'edge' at (m(a)2-m(c)2)/m(a) 18

19 MT2/MCT Harder still: two invisible particles No peak, but variables with upper limit function of involved masses: MT2 and MCT. For MT2 and MCT of 2 leptons in top and WW events 80 GeV end-point 19

20 Global variables New physics often manifests itself with the production of of heavy particles high partonic CMS for signal Transverse estimator for mass scale of new physics: scalar sum of transverse energy of all or a part of the physical objects in the event Varying nomenclature, not uniform between ATLAS and CMS: most common ones are: HT = Scalar sum of pt of jets Meff= Scalar sum of pt of all objects (include leptons and Etmiss) ST = Meff 20

21 Discovering new physics: preliminaries Once good data on disk: Calibration has to be determined and applied Detector objects to be reconstructed Reconstructed data to be made available on the grid Complete calibration loop within 48 hours of data taking Starting from reconstructed data, two steps necessary before going for new physics searches: Understanding of detector performance for main objects: leptons, jets, photons, b-jets, -jets, Etmiss Measurements of Standard Model processes to ensure that our detector understanding is adequate to look for deviations

22 Performance examples Leptons: need excellent id capabilities And resolution Jet energy scale to 2-4% for Jet PT>20 GeV B-tagging: key to detailed searches Advanced methods validated with data For 60% efficiency rejection of several hundreds On light jets

23 Etmiss measurement Key ingredient in SUSY analysis Vector sum of the measured energy deposit of all objects in the detector Any local malfunction in the detector would Be registered as a tail in Etmiss distribution From early data taking tails under control and measurement resolution in agreement with expected value 23

24 Etmiss (MET) : observed performance CMS After appropriate cleaning Little or no tails in data! CMS Good agreement of resolution with MC predictions

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26 Standard Model measurements Measured processes with σxbr down to 20 fb (ZZ->4 lep) In all cases very good agreement with SM predictions Adequate control of detector achieved No exotic source of vector/bosons top in excess of 10-20% of SM But this is only the start of the story

27 For each signal need to devise selection criteria reducing backgrounds By many orders of magnitude 27

28 Selections and backgrounds QCD jet production overwhelming at LHC, need to add something else Signatures classified in terms of non-qcd objects: leptons (e,µ), Etmiss, τ-jets, b-jets Number of QCD jets For each signature two types of backgrounds Irreducible backgrounds: basic signature identical to signal Reducible backgrounds: mimic signature because of detector effects examples: Fake Etmiss in multijet events Fake leptons For each type of background need to develop specific strategies 28

29 Flow of background evaluation 29

30 Fake Etmiss estimate 30

31 Fake lepton estimate 31

32 Irreducible backgrounds: An historical example In the 80's the UA1 experiment performed a search for Events with a single jet and Etmiss>40 GeV They found a number of events In excess of SM expectations This was interpreted as the Possible sign for the production of a 40 GeV gluino 32

33 The Altarelli Cocktail (S.Ellis, Kleiss, Stirling) Lesson: before claiming new Physics Be sure that you have considered everything in your background estimate Validate theoretical predictions using your data 33

34 Performing estimates Still Ellis Kleiss and Stirling 34

35 Background predictions: CR method DATA CR SR MC CR SR Control Region: CR No signal expected Signal Region: SR Where signal is expected Validation Region (VR) No signal expected Transfer factor (TF)= N(MC,SR)*/N(MC,CR) VR VR N(DATA,SR)=N(DATA,CR)*TF Key is clever Estimate relies on the MC to predict TF correctly Choice of CR Use VR to give you confidence on prediction 35

36 Example (as in EKS): Multijet+ Etmiss: Background from W+jets Where lepton is not detected Too low pt Outside acceptance Id inefficiency Discriminant variable: Meff: sum of pt of jets and Etmiss Leptonic decay of W well predicted by MC Measure Meff in a sample with One reconstructed lepton Predict with MC when leptons get lost 36

37 Control region by replacement Z-> νν + jets Main irreducile background to multijets+etmiss Apply all of the analysis cuts except Etmiss to a replacement process Take Z->µµ and replace leptons with Etmiss Take prompt photon events and replace photon with Etmiss Transfer the measured Etmiss spectrum in replacement process to the original process via MC MC still has a key role in transferring the result from the Replacement process to the original one Transfer is 'easy' for Z->µµ, And rather complex for prompt Photon Larger systematics Statistical error much bigger For Z->µµ 37

38 Background estimate: ABCD Method In a search for mono-photon+etmiss, main background is Z->νν+jets where the jet is identified as a photon Photons are distinguished from jets with two criteria: Identification based on shower shape and track veto Isolation: no activity in a cone around the photon By releasing one or both of these criteria Create 3 control regions In principle does not depend on MC, but typically does not work exactly and Requires MC correction 38

39 The final part: discovery/exclusion Based on all of the procedures described up to now we have two numbers: Expected number of events in the SR This is not only a number, but it includes a likelihood function expressing our degree of belief in the estimate we performed Observed number of events in SR Next step is to use statistics to assess whether The two numbers are not statistically consistent and observed>predicted DISCOVERY The two numbers are not statistically consistent and observed<predicted Something went badly wrong in the estimate, restart from scratch The two numbers are statistically consistent set a limit on the visible cross-section 39

40 From upper limits to exclusion plots Statistical procedures used in LHC experiments are extremely complex, implemented in huge black-box programmes Entire schools are devoted to it... Thus for our purpose, let's assume that from the expected number of events and the observed we are able to calculate the number of events which are excluded at 95% CL for a given SR definition This number is quoted in two forms: Expected exclusion: number of events we would exclude in case we would observe in the SR exactly the predicted number of events Observed exclusion: the actual exclusion computed with the observed number of events The expected exclusion comes with an error The comparison of the expected exclusion, its error and observed one gives an idea of the reliability of the analysis How do we transform this model-independent number into an exclusion plot for a given BSM theory? 40

41 Armed with upper limit on number of expected events, and with a Monte Carlo simulation for a new physics process Calculate via Monte Carlo the acceptance α, i.e. the probability that one event passes the kinematic selection cuts (e.g. 1 muon with pt>20 GeV and η <2.5 accompanied by a jet with pt>50 GeV and η <4.5) Not all produced muons/electron/jets are reconstructed, efficiency ε for reconstruction estimated via a mix of full simulation and measurement on data (see tag and probe) Use the measured luminosity L We obtain an upper limit on the production cross-section of a new particle X: 41

42 Finally, use theory to compute the cross-section of the hypothesized X as a function of some key parameters e.g its mass 42

43 A real-life example Acceptance and efficiency may also depend on the pramaters of the model, e.g. the mass of the particle In this case the limit is a function of the mass of the particel Between 1.5 and 2 TeV Observe a 2-s excess of data with respect to prediction Between 2.5 and 3.5 TeV Observe 2-s deficit of data With respect to prediction Expected limit: 2.75 TeV Observed limit: 3 TeV 43

44 How does a discovery look like? In this case in abscissa Instead of a cros-section Use ratio to a given Reference cross-section 44

45 Conclusions We expect new physics to exist It should manifest itself at the TeV scale the LHC is the ideal machine for this quest Discovering new physics is a long path requiring Excellent detector understanding Excellent Standard Model understanding Techniques to predict the amount of SM events in extreme kinematic regions where Monte Carlo estimates are of little help The work on the Run 1 of LHC has shown that both ATLAS and CMS are up to this challenge In the next two lectures specific examples of how we do it 45

46 ATLAS Length : ~ 46 m Radius : ~ 12 m Weight : ~ 7000 tons ~108 electronic channels ~ 3000 km of cables Inner Detector ( η <2.5, B=2T) : -- Si pixels and strips -- Transition Radiation Detector (e/π separation) Calorimetry ( η <5) : -- EM : Pb-LAr -- HAD: Fe/scintillator (central), Cu/W-LAr (fwd) Muon Spectrometer ( η <2.7) : air-core toroids with muon chambers And ~2800 physicists from 169 Institutions, 37 countries, 5 continents 46

47 LHC pp s = 14 TeV Ldesign = 1034 cm-2 s-1 (after 2012) s = 7/8 TeV Linitial < few x 1033 cm-2 s-1 (before 2012) Note: s is x7 Tevatron, Ldesign is x30 Tevatron Heavy ions ATLASand and CMS CMS: : ATLAS generalpurpose purpose general TOTEM LHC 27 km ring (previously used for the LEP e+e- collider) Here: concentrate on ATLAS ALICE : ion-ion, p-ion LHCb : pp, B-physics, CP-violation 47

48 Standard model measurements With increasing statistics measure rarer Standard model processes 48

49 Collected luminosity and detector performance Outstanding Performance from Both LHC and detector DAQ efficiency: 93.5% (5.25/5.61) 49

50 Performing estimates Need to predict number of data in Signal Region (SR) for each main background source: Define Control Region (CR) as near as possible to the SR but with negligible contribution from sought for signal and dominated by a specific background source Number of events in SR determined by the formula N(DATA,SR)=N(MC,SR)*N(DATA,CR)/N(MC,CR) Define validation region (VR) with no signal contamination and verify that the extrapolation from CR to VR works OK Role of validation region is essential, as often extrapolation from well known kinematic region to more difficult one Systematic of estimate determined by uncertainty on Transfer Factor=N(DATA,CR)/N(MC,CR) 50

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