track reconstruction challenges for future linear collidersfor future linear colliders ctd2015, lbnl...
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Track reconstruction challenges for future linear colliders
CTD2015, LBNL February 10, 2015
Norman Graf SLAC
Linear Collider Environment
• Detectors designed to exploit physics discovery potential of e+e- collisions at √s ~ 0.5 – 1(3)TeV.
• Perform precision measurements of complex final states with well-defined initial state:
– Tunable energy – Momentum constraints – Known quantum numbers
• e , e+ polarization
– Very small interaction region • “Democracy” of processes and
lower cross sections, plus precision measurements, require sensitivity to all decay channels. – W/Z separation in hadronic decays – Jet flavor tagging
2 √s (GeV)
σ(f
b)
ILC Beam Structure
• Beam structure allows for power pulsing – reduce power between bunch “trains” – reduces cooling needs
• Beam structure requires bunch disambiguation – multiple readouts during train – time-stamping of subdetector hits – detectors and algorithms capable of handling full train
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366 ns
2625x
0.2 s
0.96 ms Multiple collisions
CLIC (3TeV) 312 bunches 0.5 ns 50Hz
ILC Beam
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R (c
m)
Z (cm)
5 Tesla
“Pinch” of beams increases luminosity, but disruption creates pairs via beamstrahlung.
High field required to stay clear of “cone of death”.
Detector Requirements
• Precision invariant mass resolution – Higgs recoil measurement for Z → e+e- , µ+µ-
– Fully reconstruct hadronic final states for W/Z ID & separation • Tag quark flavor with high efficiency and purity.
– top quark Yukawa coupling ( 8 jets, 4 b), higgs self-coupling • Excellent missing energy/mass sensitivity.
– SUSY LSP • Require:
– Excellent vertexing capabilities: σrϕ ≈ σrz ≈ 5 ⊕ 10/(psin3/2ϑ)µm • Inner radius close to beampipe, high precision, time resolved
– Exceptional momentum resolution: σ(1/ pT ) = 2 ×10−5 (GeV −1) • High magnetic field, low-mass precision tracker
– Precision calorimetry: σEjet / Ejet ≈ 3% • “Particle Flow”, imaging sampling calorimeter
– Hermeticity: Ω = 4π • Minimal supports, on-detector readout.
• Affordable! → cost-constrained optimized design
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Momentum Resolution Driver
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Recoil Mass
Tagged sample of Higgs events. Provides sensitivity even to invisible decays. Goal is δp⊥/p⊥
2 ~ 2x10-5
Two complementary solutions: Large number of lower resolution hits or small number of precise hits. ILD SiD
ILD
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ILD Tracking System
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TPC
SET
VTX/SIT
ETD
FTD
3.5T
Clupatra TPC pattern recognition
• NN cluster in pad row ranges → clean track stubs – Extend inward /outward using Kalman Filter
• Repair split tracks / merge segments
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Silicon, Forward and Full Tracking
• Silicon Tracking – brute force triplet search in stereo angle sectors based on a set of seed-
layer-triplets – road search based on helix fit – attach leftover hits – refit
• Forward Tracking – Cellular Automaton for track finding – Hopfield Networks to arbitrate between candidates with mutual hits – Subset processor to find consistent set with tracks from Silicon
Tracking • Full Tracking
– combines track from TPC-Silicon-Forward tracking based on track parameter compatibility
– adds spurious leftover hits – final track fit
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ILD Track Efficiency
• e+e-→ttbar events: primary particles from within 10 mm of IP that leave at least 4 hits in detector and reach the calorimeter
• included full background from incoherent pair production - O(106) hits in VXD !
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ILD Track Resolutions
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5 3
1/2 10 1 10
sinTpTGeV p
σθ
− −× ×= ⊕ 3/2
105( )sinr m m
p GeVϕσ µ µθ
= ⊕
Silicon Detector (SiD)
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SiD Design Concept
• Although mechanically and technologically distinct, the vertex detector and outer tracker are being designed as an integrated system.
• Expect best performance with a uniform technology • Si and CF support allows for uniform material • Also allows for easy optimization of the design • Superior point resolution • Provides single bunch crossing timing • Robust against beam backgrounds and field
nonuniformities 14
SiD Tracking Detectors
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Vertex: 5 barrel + 7 disk inner pixel detector 20µm x 20µm Tracker: 5 barrel (axial strip) 4 disk (stereo strips) 5T Central Field
SiD Tracking Detectors
• Material budget X/X0 < 0.1 in central region, <0.2 throughout the tracking volume.
• Uniform coverage of a minimum of 10 hits per track down to small angles. 16
SiD Track Finding Strategy
• Circle fit to three “seed” layers provides initial track fit
• “Confirm” layer provides fast fail • “Extend” layers add remaining hits • StrategyBuilder creates list of topological sets of
“seed” and “confirm” layers. – Developed using MC training samples – Run as many strategies as are needed
• Inside-out strategies currently being used – two innermost vertex layers excluded due to high
occupancies 17
SiD Track Efficiencies
• 1TeV Z’ → qqbar + pairs + γγ→hadrons
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SiD Track Resolutions
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5 3
1/1.5 10 2.2 10
sinTpTGeV p
σθ
− −× ×= ⊕ 2D impact parameter < 2µm
Non-prompt Tracks
• Decays of K0S, Λ, conversion, long-lived exotics,…
• Calorimeter Assisted Tracking (garfield) • Fine-grained Ecal (30 layers, 3.5mm pixels provides excellent MIP
tracking. • Find MIP-stubs, extend into tracker
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CLIC Tracker Optimization
• Efforts currently underway to optimize tracker layout – R=1.25m → 1.5m – Adding extra endcap disks – Longer Barrel (L/2=1.6m → 2.3m) – Layer layout optimization – Revisiting timing and occupancy – Studying effects of inhomogeneous field
• Basically, the design proposed for 0.5 – 1 TeV proved workable.
• No major change in pattern recognition needed. – Investigating cellular automaton & minivectors
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Are we done?
• Both ILD and SiD at the ILC and the CLIC detector have demonstrated (with MC) that they can achieve the required detector performance using existing pattern recognition algorithms.
• Tracking results are predicated on being able to maintain the material budgets currently envisioned – Need to ensure robustness against material creep
• Development work ongoing to improve algorithms or CPU performance
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ILD Tracking Developments
• Investigating Fine Pixel CCDs (5µm pixels) in vertex detector to reduce occupancy accumulated during one train.
• Work ongoing to develop algorithms to identify clusters arising from low pT backgrounds.
• Investigating application of Cellular Automaton to central Si-Tracking. – higher efficiencies at lower CPU look promising
• Smarter seeding in outer Si layers to improve CPU
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SiD Tracking Developments
• Optimizing tracker and vertex layout: number, lengths and positions of layers.
• Replacing forward shallow stereo with pixel layers – Removes ghost hits, reduces material
• Investigating strixels for outer tracker • Investigating all-pixel tracker • More, better 3D spacepoints will allow adoption of
different pattern recognition algorithms – e.g. conformal mapping
• Implementing non-uniform field handling 26
Tracking Software Plans
• LC Community shares a common event data model and persistency format (LCIO) – Makes exchange of software easier – Fortran event generator, Java tracking, C++ PFA,
python analysis… • Work ongoing within AIDA (⇒ Horizon2020) to
provide a common geometry system (DD4hep) and common tracking software infrastructure (AidaTT)
• Refactoring, rewriting, incorporating new ideas – Perfect time to contribute!
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And much more…
• Additional pattern recognition problems being tackled within the LC community include:
• Flavor-tagging using displaced vertices – LCFIPlus
• Calorimeter clustering and track-cluster association – PandoraPFA
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Flavor Tagging
• Tagging of charm and bottom quarks important for many studies including Higgs branching ratios.
• Flavor-tagging of jets based on displaced vertices • LCFI package based on SLD’s ZVTOP
topological algorithm. – Required jet-finding to provide direction
– Flavor tagging based on Neural Networks
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LCFIPlus
• Multivariate analysis: • BDT in place of NN • Separated by # of vertices • Vertex position/mass/tracks • Impact parameters of tracks • ~ 20 variables
• c-tag depends on vertex resolution
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• Vertices built up from complete set of tracks • Does not require jet direction (avoid jet
ambiguity) better in multi-jet environments (ZHH etc.)
• Single track can be assigned to second vertex to identify b-c cascade decay IP
Secondary vertex
Single track vertex (nearest point)
Vertex-IP line
track
D θ
Jet Energy Resolution
• Many interesting physics processes involve multi-jet final states.
• Reconstruction of dijet invariant mass important for event reconstruction and ID (e.g. WW vs ZZ)
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• Beamstrahlung reduces value of kinematic fits, puts premium on intrinsic detector resolution
Individual Particle Reconstruction
• ~60% of jet energy from charged particles • ~30% photons • ~10% neutral hadrons • Highly granular “imaging” calorimeters should enable
unambiguous association of showers with individual particles.
• Associating clusters with charged tracks allows momentum measurement of tracker to be used instead of energy measurement of calorimeter
• Photons measured in Ecal ~20% • Remaining neutral hadrons measured in Hadron calorimeter • Reducing “confusion” term relies on excellent calorimeter
granularity and tracking + flexible set of sophisticated clustering and cluster-track association algorithms 32
PandoraPFA
• Developed by Mark Thomson and John Marshall at Cambridge for ILD, subsequently applied to SiD and CLIC detectors. Delivers 3-4% energy resolution →2.5σ W/Z sep
• Now finding application outside of collider detectors.
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Reconstruction
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Reconstruction
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Conclusion
• Two complementary approaches have been adopted in the LC community to provide tracking – ILD’s large TPC with Si envelope and vertex tracker – SiD’s all-silicon strip + pixel system
• Both have been shown to provide the performance required by the aggressive ILC (.5–1 TeV) physics program.
• CLIC has demonstrated the applicability of the all-silicon approach at higher energies (3TeV)
• Ongoing program to improve the software, adopt new algorithms and attack new problems.
• Tracking results feed into both flavor-tagging and PFA, both areas of active algorithm development.
• We look to learn from existing experiments and expect to contribute to new efforts. 36