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