Jet Substructure In ATLAS

Transcription

1 Jet Substructure In ATLAS INFN Pisa & University of Arizona Parton Showers & Event Structure At The LHC (Northwest Terascale Research Projects Workshop) University of Oregon February 23-27, 2009

2 Overview Motivation and challenges Physics interest in jet mass and substructure General considerations for detector jets The ATLAS detector Brief overview Jet reconstruction sequences in ATLAS Calorimeter signal reconstruction Using tracks in jet reconstruction Experimental environment Pile-up in ATLAS Expectations for experimental sensitivities to jet substructure Conclusions/outlook Slide 2

3 Jet Mass & Substructure Gained interest at LHC Decay products of highly boosted heavy particles all reconstructed as one (narrow) jet E.g. top quark Indication of source from jet mass requires high resolution of spatial structures Jet mass measurement notoriously difficult due to (hadronic( hadronic) shower spread Substructure variables more useful? E.g. y-scaley scale We try to understand sensitivities Dependence on calorimeter signal choice Prominent constituent reconstruction Other sub-structures? structures? 2 light quark jets from W decay and 1 b-jet See Jon Walsh s s talk this afternoon for more physics motivations! Slide 3

4 Image of Particle Jets in Detectors 10 GeV 1 GeV 100 MeV Slide 4 Change of composition Radiation and decay inside detector volume Randomization of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

5 Image of Particle Jets in Detectors 10 GeV 1 GeV 100 MeV Slide 5 Change of composition Radiation and decay inside detector volume Randomization of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

6 Image of Particle Jets in Detectors 10 GeV 1 GeV 100 MeV Slide 6 Change of composition Radiation and decay inside detector volume Randomization of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

7 Image of Particle Jets in Detectors 10 GeV 1 GeV 100 MeV Slide 7 Change of composition Radiation and decay inside detector volume Randomization of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

8 Image of Particle Jets in Detectors 10 GeV 1 GeV 100 MeV + c c c c c c c c c c c c + + Slide 8 Change of composition Radiation and decay inside detector volume Randomization of original particle content Defocusing changes shape in lab frame Charged particles bend in solenoid field Attenuation changes energy Total loss of soft charged particles in magnetic field Partial and total energy loss of charged and neutral particles in inactive upstream material Hadronic and electromagnetic cacades in calorimeters Distribute energy spatially Lateral particle shower overlap

9 (Experimental) Considerations Jet mass and substructure reconstruction Unfold calorimeter shower shapes as much as possible Use calorimeter signal definition following particles Provide stable estimators for chosen variables Electronic and pile-up noise Control/understand fluctuations in jet signal composition due to UE and PU contributions Reconstruction considerations Physical meaningful substructure linked to certain jet algorithm E.g. y-scale y only meaningful for kt jets kt not a favorite from experimental point of view vacuum cleaner effect,, sensitivity to noise/pile-up But can re-run run kt clustering on constituents of Anti-kT kt, SISCone,, etc. jets!! Y-scale comes from the last n recombinations,, i.e. hardest part of jet! May have to focus on harder part of jets in general Mass from constituents above threshold only (limit from m=2 n hardest constituents) Slide 9

10 The ATLAS Detector Total weight : 7000 t Overall length: 46 m Overall diameter: 23 m Magnetic field: 2T solenoid + toroid Slide 10

11 EM Barrel EMB Hadronic Endcap Tile Barrel EM Endcap EMEC Forward The ATLAS Calorimeters Electromagnetic Barrel η < 1.4 Electromagnetic EndCap < η < 3.2 Hadronic Tile η < 1.7 Hadronic EndCap 1.5 < η < 3.2 Forward Calorimeter 3.2 < η < 4.9 Tile Extended Barrel Varied granularity Varied technologies Overlap/crack regions Slide 11

12 Electromagnetic Calorimetry Highly segmented lead/liquid argon accordion No azimuthal cracks 3 depth segments + pre-sampler (limited coverage) Strip cells in 1 st layer Very high granularity in pseudo- rapidity Δ η Δϕ Deep cells in 2 nd layer High granularity in both directions Δ η Δϕ Shallow cells in 3 rd layer Δ η Δϕ Electromagnetic Barrel Slide 12

13 Hadronic Calorimetry Tile calorimeter Iron/scintillator tiled readout 3 depth segments Quasi-projective readout cells First two layers: Δ η Δ ϕ Third layer Δ η Δ ϕ Very fast light collection ~50 ns Dual fiber readout for each channel Slide 13

14 EndCap Calorimeters Electromagnetic Spanish Fan accordion Highly segmented with up to three longitudinal segments η < 2.5, middle layer Δ η Δ ϕ < η < 3.2 Hadronic liquid argon/copper calorimeter Parallel plate design Four longitudinal segments Quasi-projective cells η < 2.5 Δ η Δ ϕ < η < 3.2 Slide 14

15 Forward Calorimeters Design features Compact absorbers Small showers Tubular thin gap electrodes Suppress positive charge build-up up (Ar+) in high ionization rate environment Stable calibration Rectangular non-projective readout cells Δ η Δ ϕ Electromagnetic FCal1 Liquid argon/copper Gap ~260 μm Hadronic FCal2 Liquid argon/tungsten Gap ~375 μm Hadronic FCal3 Liquid argon/tungsten Gap ~500 μm FCal1 FCal3 FCal2 Slide 15

16 Collecting Cells: Towers Imposes regular grid view on event ( Δ η Δ ϕ = ) Motivated by event ET flow Natural for trigger! Calorimeter cell signals are summed up in tower bins Default: no cell selection, all cells are included Indiscriminatory signal sum includes cells without any true signal at all Sum typically includes geometrical weight Towers have fixed direction Massless four-momentum representation on electro- magnetic energy scale (,, ) (,,, ) with Slide projective cells E E p p p p E p p p ηϕ ηϕ ηϕ x y z = ηϕ = x + y + z w cell 1.0 w cell non-projective cells E ηϕ = ηϕ w celle 0, 0, cell ηϕ ηϕ ( A cell cell A ηϕ ηϕ) 0 ηϕ ηϕ 1 if A cell Δ η Δ ϕ = ηϕ ηϕ < 1 if A cell >Δ η Δ ϕ

17 Topological Cell Clusters (1) Motivation Attempt reconstruction of individual particle showers Reconstruct 3-dim 3 clusters of cells with correlated signals Use shape of these clusters to locally calibrate them Explore differences between electromagnetic and hadronic shower development and select best suited calibration Supress noise with least bias on physics signals Often less than 50% of all cells in an event with real signal Some implications of jet environment Shower overlap cannot always be resolved Clusters represent merged particle showers in dense jets Clusters have varying sizes No simple jet area as in case of towers Clusters are massless 4-vectors (as towers) No artificial mass due to showering Slide 17

18 Topological Cell Clusters (2) Cluster seeding Cluster seed is cell with significant signal above a primary threshold Significance = signal-over over-noise (may include PU noise) Cluster growth: direct neighbours Neighbouring cells (in 3-d) 3 with cell signal significance above some basic threshold are collected Basic threshold = 0 presently, i.e. all neighbouring cells Cluster growth: control of expansion Collect neighbours of neighbours for cells above secondary signal significance threshold Seconday threshold lower than primary (seed) threshold Cluster splitting Analyze clusters for local signal maxima and split if more than one found In 3-d, 3 again Final energy blob can contain low signal cells Cells survive due to significant neighbouring signal Cells inside blob can have negative signals Slide 18

19 Topological Cell Clusters (3) cluster candidate #1 #3? cluster candidate #2 Slide 19

20 Calorimeter Signal Definition Affects Jet Shape Slide 20

21 Recall Jet Composition Charged particles carry large fraction of total jet energy on average ~60% from charged pions, Kaons,, protons These particles can leave a track in the inner detector Momentum (pt( pt) ) measurement pt fraction carried by reconstructed tracks is observable They are all hadrons Jet with a large fraction of pt carried by charged particles is more hadronic Sensitivity of calibrated calorimeter signal to this fraction? Slide 21

22 Track Jets Aim: relative improvement of the jet energy resolution Jet-by by-jet correction Reconstruct jets from inner detector tracks Match track jets with calorimeter jets Calculate pt fraction carried by tracks Determine correction as function of pt fraction ATLAS MC (preliminary) f trk = p p Ttrack, Tcalo, Slide 22

23 Jets Not From Hard Scatter Dangerous background for W+n jets cross-sections sections etc. Lowest pt jet of final state can be faked or misinterpreted as coming from underlying event or multiple interactions Underlying event: multi-parton interactions Multiple interactions: pile-up Extra jets from UE are hard to handle No real experimental indication of jet source Some correlation with hard scattering No separate vertex Jet-by by-jet handle for multiple interactions Classic indicator for multiple interactions is number of reconstructed vertices in event Tevatron with RMS(z_vertex) ) ~ 30 cm LHC RMS(z_vertex) ) ~ 8 cm If we can attach vertices to reconstructed jets, we can in principle identify jets not from hard scattering Limited to pseudorapidities within 2.5! Slide 23

24 Application of Trackjets Match tracks in track jet with calorimeter jet Calculate pt fraction coming from each vertex for given jet Jets with little pt from primary vertex are likely from multiple interactions (e.g. pile-up) ATLAS MC (preliminary) ATLAS MC (preliminary) Slide 24

25 LHC Environment: Pile-Up Multiple interactions between partons in other protons in the same bunch crossing Consequence of high rate (luminosity) and high proton- proton total cross-section section (~80-~100 ~100 mb) Statistically independent of hard scattering But similar models used for soft physics Signal history in calorimeter increases noise Signal times slower than bunch crossing rate (25 ns) Noise has coherent character Cell signals linked through past shower developments without pile-up E t ~ 81 GeV E t ~ 58 GeV Slide 25

26 LHC Environment: Pile-Up Multiple interactions with design luminosity between partons in other pile-up E t ~ 81 GeV protons in the same bunch Calorimeter crossing signal effected by PU in Consequence of high rate a window (luminosity) of ~625 and ns, high i.e. proton- ~25 bunch Xings proton (23 total history, cross-section section 1 in-time, 1 E t ~ 58 GeV following) (~80-~100 ~100 mb) need about Statistically independent of 25*(~20-~24) hard ~24) scattering (poisson-distributed) But similar statistically models used for soft independent physics Signal history in fully calorimeter simulated min increases bias events noise to simulate Signal the detector times effect slower of PU on than bunch crossing rate (25 1 signal ns) event at 10 34! (and Noise need has lots of coherent those to avoid long range character correlations in PU between signal Cell signals events!) linked through past shower developments Slide 26

27 LHC Environment: Pile-Up Multiple interactions between partons in other protons in the same bunch RMS( p T ) (GeV) crossing Consequence of high rate (luminosity) and high proton- proton total cross-section section (~80-~100 ~100 mb) Statistically independent of hard scattering But similar models used for soft physics Signal history in calorimeter increases noise Signal times slower than bunch crossing rate (25 ns) Noise has coherent character Cell signals linked through past shower developments L = 10 cm s 8 GeV = cm 2 s 1 18 GeV R 0.4 R 0.7 π ( ) R Slide 27

28 Jet Masses Mass measurement challenging Particle jet level mass is reference Simulations only! Mass of calorimeter jet is affected by shower spreads Enters: signal definition dependence, cluster shapes/overlap, noise, Sensitivity to losses of soft particles in unbiased mass reconstruction Magnetic field, dead material, Can we reconstruct jet masses from principal constituents? Instead of all constituents cluster jets tower jets cluster jets tower jets cluster jets tower jets ATLAS MC (preliminary) y < < y < < y < 4.2 ( ) M M M jet jet jet rec true true Slide 28

29 Mass Reconstruction Sensitivities (1) Contribution from low energetic particles lost Overall effect depends on signal definition How about effect on mass? Exercise: remove particles below pt threshold from jet and re-calculate mass Remember: towers are not calibrated More severe effect of cut in tower jets Clusters are calibrated More similar to particle selection in jets Slide 29

30 Mass Reconstruction Sensitivities (2) change of mass QCD kt jets, D = 0.6 Slide 30 log 10 (least biased reconstructed mass/gev)

31 Jet Composition (1) 1 st question: any relation between number of particles, towers, clusters in jets? Most interesting for kt D = 0.6 here Look at matching callorimeter/truth jets Note: not the most important variable! We already expect change of jet picture by detector signal definition Hints on resolution power for jet shape variables and mass Slide 31

32 Jet Composition (2) We expected clusters to represent indivdual particles Cannot be perfect in busy jet environment! Shower overlap in finite calorimeter granularity Some resolution power, though Much better than for tower jets! ~1.6:1 particles:clusters in central region ~1:1 in endcap region Best match of readout granularity, shower size and jet particle energy flow Happy coincidence, not a design feature of the ATLAS calorimeter! Slide 32

33 Cluster-Particle Matching (1) Jet of dispersed particles Many clusters Lateral jet structure well resolved Substructure reconstruction should be possible No significant shower overlap in calorimeter Full simulation, QCD 2-to2 to-2, EM calorimeter central layer Slide 33

34 Cluster-Particle Matching (2) Jet of close-by particles Most energy in one cell cluster Cluster is rather narrow in itself Little to no substructure reconstruction possible Shower overlap cannot be resolved in calorimeter granularity Full simulation, QCD 2-to2 to-2, EM calorimeter central layer Slide 34

35 Cluster-Particle Matching (3) Jet of close-by particles Energy shared in clusters staggered in depth due to hadronic shower development Indication of good resolution power Can reconstruct some substructure? Each cluster is a 4-vector! 4 Full simulation, QCD 2-to2 to-2, EM calorimeter central layer Slide 35

36 Sidetrack: UE With Clusters I. Vivarelli Immediate enhancement: Look at TransMIN, TransMAX separately! Slide 36

37 Jet Energy Density Slide 37

38 Jet Substructure Mass too complex? Can be too sensitive to small signals in jets UE, pile-up, other noise Use YSplitter to detect substructure Determines scale y for splitting a giving jet into 2,3, subjects, as determined by y cut, from y = y p jet cut jet T More stable as only significant constituents are used? At least additional information to mass Other option: Look at mass of 2 n 2 n hardest constituents (Ben Lillie,ANL) Not very sensitive to calorimeter signal details! Slide 38

39 Conclusions Slide 39

40 Conclusion/Outlook Determination of jet origin from experimental (calorimeter) observables feasible Systematic evaluation of boosted heavy particle missing Not high on the list right now? Clear preference for cluster signal for jet constituent based quantities Seems to better follow jet composition Principal constituents based reconstruction of jet shapes needs studied Good chance of increased stability in the presence of pile- up Loss/gain of sensitivity for y-scale, y mass, to be explored Need to include track jets into picture! Slide 40