LHC Experiments: Hadronic Jet Physics

Transcription

1 LHC Experiments: Hadronic Jet Physics Jason Nielsen Santa Cruz Ins>tute for Par>cle Physics University of California, Santa Cruz Theore>cal Advanced Studies Ins>tute Boulder, Colorado June 2010

2 Jets as Observed Objects Partons are nice for calcula6on, but jets are real Result of boosted partons hadronizing at Λ QCD scale We do not expect a perfect one- to- one correspondence, except possibly for high- p T well- separated partons Rela6on between the two should be calculable on avg Experimentalists are becoming more savvy about jet algorithms and how to make best comparisons between measurements and theore6cal predic6ons Bleeding- edge NLO calcula6ons are omen parton- only, so seek a middle ground in which to compare results J.Nielsen TASI

3 Fragmenta6on vs. Jet Clustering Physical process from parton to hadrons is hadroniza6on; in jet clustering, we seek to reverse the physical process Both of these depend on the art of non- perturb. QCD Three parts of the jet reconstruc6on algorithm: Which input par6cles to cluster? Truth, cells, clusters? Which par6cles should be combined into a jet? How should the par6cle 4- momenta be combined? (This is simple: add 4- momenta vectorially) Look for jet algorithms that are fast, robust under par6cle boosts (along z), collinear and infrared safe J.Nielsen TASI

4 Cone Algorithm Overall approach: combine nearby par6cles, but near in space or some other distance metric? Cone algorithm defines distance Define seed par6cle, typically highest p T par6cle Combine par6cles inside a cone wrt seed momentum Well- defined geometrically but not collinear safe J.Nielsen TASI

5 Sequen6al Recombina6on Algorithms Class of algorithms that combine nearest par6cles first, instead of fixing seed and cone (mimic parton shower) Cambridge/Aachen algorithm: combine par6cles nearest each other in (η,φ) space (minimum d ij ) kt algorithm: preference for combining lower- momentum par6cle pairs first These algorithms correspond to p=0 and p=1 in What about p=- 1? Is this a physical choice of p? J.Nielsen TASI

6 An6- Kt Algorithm New algorithm (Cacciari et al.) collects par6cles around the hardest par6cle first Guarantees cone- like geometry with well- defined borders around the highest- kt par6cles. Maintains the infrared safety and collinear safety of sequen6al recombina6on family Becoming a preferred jet algorithm at LHC, along with a modified stable infrared- safe cone algorithm (SISCone) J.Nielsen TASI

7 Jet Algorithm Comparison Cacciari et al. ( ) J.Nielsen TASI

8 Jet Energy Measurements Number of par6cles in shower is propor6onal to energy of the incident par6cle but not in prac6ce because Par6cle shower in hadronic calorimeter has both hadronic and EM component (π 0 ) in fluctua6ng ra6o Some par6cles are absorbed completely in absorber of sampling calorimeter (up to 30%) Varying response in different calorimeter regions Pileup collisions and underlying event (som par6cles) underneath our jet from the hard scaoer J.Nielsen TASI

9 Jet Energy Calibra6on Correct detector- level jet energy to the par6cle level Correct for varying detector response (dijet balance) due to non- linearity, uninstrumented regions (cracks) Average correc6on for EM+HAD contribu6ons (γ+jet balance assumes known EM scale energy of photon) Overall absolute scale calibra6on using γ+jet events Correct for average loss of par6cles due to B field, average gain from contribu6ons of underlying event Very high- energy jets do not have calibra6on samples Extrapolate correc6ons or use simulated events Correc6ons add uncertain6es to Jet Energy Scale (JES) J.Nielsen TASI

10 Jet Energy Systema6c Uncertain6es Systema6c because they do not depend directly on the number of events collected for measurement; they are 6ed to our system of calibra6ng the jet energies These are the largest uncertain6es for jet physics, including W/Z+jets and top quark measurements We believe the calibra6on is almost universal for all jets in the experiment CMS PAS FWD Forward Jets 3< η <5 J.Nielsen TASI

11 Detailed Detector Simula6on: GEANT GEANT toolkit simulates passage of par6cles through maoer (for example, detailed model of detectors built by experimental collabora6on) Follows par6cles step- by- step through material Includes latest low- energy interac6on models J.Nielsen TASI

12 Fast Detector Simula6on: Delphes/PGS Predict average detector response based on par6cle 4- momentum, not by following par6cle step- by- step Input par6cles from parton shower programs Output reconstructed objects : leptons, jet, miss E T Freely available for use in studies outside collabora6ons # Energy resolution for electron/photon! # \sigma/e = C + N/E + S/\sqrt{E}, E in GeV! ELG_Scen 0.05 // S term for central ECAL! ELG_Ncen 0.25 // N term for central ECAL! ELG_Ccen // C term for central ECAL! # Tagging definition! BTAG_b 40 // b-tag efficiency (%)! BTAG_mistag_c 10 // mistagging (%)! BTAG_mistag_l 1 // mistagging (%)! J.Nielsen TASI

13 Comparing Theory and Experiment NLO theory calcula6ons end at parton level, not even the par6cle level. But raw experimental results are at detector- level (measured jet mul6plicity, jet p T ) Experimentalists unfold the detector effects and quote par6cle- level (hadron- level) measurements Account for detector efficiency, energy resolu6on, angular resolu6on by finding a mapping between par6cle- level and detector- level results Cast a cri6cal eye on whether this step has been included in a published result; if not, it will be difficult to compare with theory J.Nielsen TASI

14 Unfolding Detector Effects on Jets Main effects can be calculated from detailed simula6on, using non- perturba6ve shower/fragmenta6on models Charged tracks bending out of cone/jet Extra- wide jet not fully clustered by jet algorithm Underlying event : cloud of low- p T par6cles in conjunc6on with the hard scaoer to be measured J.Nielsen TASI

15 Proper6es of Heavy Flavor Decay b quarks (& hadrons) have following special proper6es: Long- lived (due to CKM suppression) Massive with respect to decay products Semileptonic decay through spectator High mul6plicity decays Compare cτ b =500µm with cτ c =310µm and cτ τ =90µm Major difference in number of par6cles in jet Difference in masses is it important? J.Nielsen TASI

16 Geometry of Typical Heavy Flavor Decay Decay products point back to Secondary Vertex, not PV Measure the decay length L xy or impact parameter d 0 Need measurements with precision 10% x O(300µm) J.Nielsen TASI

17 Primary Vertex Reconstruc6on Essen6al to calcula6ng decay length parameters Experiments fit the PV for each event from the tracks found in the event (be careful with pileup) J.Nielsen TASI

18 Flavor Tagging Algorithms for Jets Impact parameter likelihood Product of each track s incompa6bility with PV Secondary vertex decay length significance Measures separa6on between SV and PV: L/σ L SoM lepton iden6fica6on from semileptonic HF decay Typically few GeV Vertex mass calcula6on Direct comparison of quark masses m b, m c, m uds Mul6variate techniques Include all of the above, plus track mul6plicity in jet J.Nielsen TASI

19 Typical b- tagging Performance Given discrimina6ng variable, choose purity or efficiency Typical efficiency of 50-70%, with some varia6on in p T, η J.Nielsen TASI

20 Missing Energy Reconstruc6on Opposite vector sum of all interac6ng par6cles: assume zero total transverse momentum in ini6al state Near- herme6c calorimeters measure charged & neutral Apply correc6ons for response to muons, jets Missing transverse energy (Etmiss or MET) may be due to weakly- interac6ng neutral par6cles or mismeasurement of the observed objects J.Nielsen TASI

21 Missing Energy Calibra6on Calibrate both scale and resolu6on using events known to have a specific missing energy Important to calibrate several different points to enable extrapola6on to higher values Rough Etmiss resolu6on (confirmed early data) No6ce same scaling as calorimeter resolu6on ATLAS J.Nielsen TASI

22 Triggering at Hadron Colliders Not many collisions are useful/interes6ng Fast decision to reject events at 40 MHz requires simple filters using fast detectors and logical ANDs Generic filters desirable, so that we do not miss unexpected signatures If event does not pass at least one trigger hypothesis, it is thrown away and lost J.Nielsen TASI

23 ATLAS and CMS Trigger Approaches Fast output from certain subsystems input to Lvl1 filter Event Filter and HLT farms run detailed offline algorithms 100 khz J.Nielsen TASI

24 Trigger and Signatures Individual Trigger Objects: TAU, JET, TransverseEnergy, EM, MUON with different energy thresholds Signature Example: electron Level 1: EM cluster E T > 23 GeV Level 2: matching track; π 0 and jet rejec6on cuts Signature Example: tau pair Level 1: EM cluster E T > 14 GeV && TAU E T > 52 GeV Level 2: matching tracks, missing energy requirement If rate of electron events becomes too great, then some filter has to be 6ghtened to reduce the overall rate J.Nielsen TASI

25 Defini6on of Proton- Proton Interac6ons P. Skands et al., hep-ph/ J.Nielsen TASI