1. Reconstruction of strange hadrons in collisions of nuclei at RHIC
Jakub Kubát
Research Project at Faculty of Nuclear Science and Physical Engineering

2. Faculty of Nuclear Science and Physical Engineering
Outline
Quark-gluon plasma
STAR + fixed target experiment
Intro to KF Particle framework
Results
Au+Au at 27 AGeV (KF Particle Finder + TMVA)
Au+Au at 14.6 AGeV (KF Particle Finder)
Fixed target at 3.9 AGeV (KF Particle Finder)
Summary
Faculty of Nuclear Science and Physical Engineering

3. Faculty of Nuclear Science and Physical Engineering
Quark-gluon plasma
QGP – new state of matter at high temperatures or energy densities
Experimental evidence
RHIC, LHC
Strangeness enhancement, suppression of jets, quarkonia, flow, …
Transition between QGP and hadron gas in QCD diagram
Nature? Critical point?
Mapping out QCD diagram – collisions at various energies (different baryon chemical potential 𝜇 𝐵 )
Increasing luminosity at low 𝑠 𝑁𝑁 - BES, fixed target, CBM
Faculty of Nuclear Science and Physical Engineering

4. Faculty of Nuclear Science and Physical Engineering
Experiment STAR
Au+Au at 27 AGeV (2018)
FXT at 3.9 AGeV
iTPC
Continuous pad rows
Replace all inner TPC sectors
Better dE/dx and momentum resolution
𝜂 <1.5 (was 𝜂 <1.0)
𝒑 𝑻 >𝟔𝟎 MeV/c (was 𝒑 𝑻 >𝟏𝟓𝟎 MeV/c)
Au+Au at 14.6 AGeV (2019)
Faculty of Nuclear Science and Physical Engineering

5. Faculty of Nuclear Science and Physical Engineering
Intro to KF Particle
Package developed for complete reconstruction of short- lived particles and their parameters by FIAS group for CBM
Based on Kalman Filter
Describes particles with state vector and covariance matrix
Covariance matrix reflects track errors and detector efficiency
Enables to calculate statistic criteria which can be used for background rejection
Independent of geometry - successful implementation at STAR
Other properties
Treats mother and daughter particles in the same way
Allows reconstruction of decay chains
Fully SIMDized
KF Particle — S. Gorbunov, “On-line reconstruction algorithms for the CBM and ALICE experiments,” Dissertation thesis, Goethe University of Frankfurt, 2012,
KF Particle Finder — M. Zyzak, “Online selection of short-lived particles on many-core computer architectures in the CBM experiment at FAIR,” Dissertation thesis, Goethe University of Frankfurt, 2016,
KF Particle Tutorial – M. Zyzak,
Faculty of Nuclear Science and Physical Engineering

6. Lambda with KF Particle
With covariance matrix full information about track parameters is available
Parameter uncertainties are used to calculate statistical criteria based on 𝜒 2 distribution
Within KFP framework one cuts on probabilities (params. normalized on error) rather than absolute values of topological variables
Statistical approach expected to be more effective for multi-strange baryons (cascades)
Default KFPF cuts
𝜒 𝑓𝑖𝑡 2 <10
𝑙/𝑑𝑙>5
𝑙>5 cm
𝜒 𝑝𝑟𝑖𝑚 2 >18.6
𝜒 𝑡𝑜𝑝𝑜 2 <5
𝑑 𝑚𝑎𝑥 <1 cm
𝜒 𝑓𝑖𝑡 2
𝜒 𝑡𝑜𝑝𝑜 2
𝑙/𝑑𝑙
𝜒 𝑝𝑟𝑖𝑚 2
Faculty of Nuclear Science and Physical Engineering

7. Faculty of Nuclear Science and Physical Engineering
Au+Au at 27 AGeV
Run 2018, Au+Au 27 GeV
Production P19ib, library SL19b
MB events after selection: 370M
𝑉 𝑧 <70 cm
More than 10% primary tracks
Faculty of Nuclear Science and Physical Engineering

8. Faculty of Nuclear Science and Physical Engineering
Lambda extraction with KF Particle Finder
Default KFPF cuts
𝜒 𝑓𝑖𝑡 2 <10
𝑙/𝑑𝑙>5
𝑙>5 cm
𝜒 𝑝𝑟𝑖𝑚 2 >18.6
𝜒 𝑡𝑜𝑝𝑜 2 <5
𝑑 𝑚𝑎𝑥 <1 cm
Signal extracted from 0.2 to 6.0 GeV/c
Lowest 𝑝 𝑇 = 0.2 – 0.4 GeV/c, while published 𝑝 𝑇 =0.4−0.6 GeV/c
Fitting:
double Gaussian with polynomial background
Faculty of Nuclear Science and Physical Engineering

9. Faculty of Nuclear Science and Physical Engineering
TMVA training
Cuts for training
𝜒 𝑓𝑖𝑡 2 <14
𝑙/𝑑𝑙>3
𝑙>1 cm
𝜒 𝑝𝑟𝑖𝑚 2 >3
𝜒 𝑡𝑜𝑝𝑜 2 <5
𝑑 𝑚𝑎𝑥 <1 cm
Soft ToF PID mode
Further improvement with Boosted Decision Trees
Learning
Looser particle cuts in KFPF
Signal: VMC pure signal simulation, thermal pT spectrum
Background: Sidebands from data sample (20%)
Significance scan
Applied BDT on different sample (3%) and searched for ideal BDT cut value
Used TMVA::Reader directly in KFPF to apply weights on whole statistics
Faculty of Nuclear Science and Physical Engineering

10. KFP: TMVA vs. default cuts
𝒑 𝑻 = 0.1 – 0.2 GeV/c !
standard geom. cuts
This thesis: KFP + BDT
𝒑 𝑻 = 0.1 – 0.2 GeV/c !
KFP: TMVA vs. default cuts
TMVA improves significance throughout whole 𝑝 𝑇 spectrum
Also enables reconstruction at very low 𝑝 𝑇 =0.1−0.2 GeV/c
KFP more effective than conventional analysis with topological cuts
Default cuts
BDT application
Analysis note -
Invariant mass plots -
Faculty of Nuclear Science and Physical Engineering

11. Faculty of Nuclear Science and Physical Engineering
Au + Au at 14.6 AGeV
Run 2019, Au+Au 14.6 AGeV
Express production with iTPC
No official data production yet
MB events after selection: 200M
𝑉 𝑧 <70 cm
More than 10% primary tracks
Faculty of Nuclear Science and Physical Engineering

12. Sidenote: express production at STAR
High Level Trigger, Tracking Focus Group
STAR HLT uses high performance computers to do real time event reconstruction and analysis
Provide additional event selection capability based on physics analysis on top of hardware trigger layers
Beam line monitoring
Online 3D primary vertex reconstruction
Real time beam position monitoring in RHIC low energy runs
Live feedback to CA for accelerator monitoring and performance tuning
Benefited all BES-I physics analyses
Likely to be used again in BES-II
Faculty of Nuclear Science and Physical Engineering

13. Faculty of Nuclear Science and Physical Engineering
Au + Au at 14.6 AGeV
Run 2019, Au+Au 14.6 AGeV
Express production with iTPC
No official data production yet
MB events after selection: 200M
𝑉 𝑧 <70 cm
More than 10% primary tracks
Faculty of Nuclear Science and Physical Engineering

14. Signal reconstruction
Reconstruction was done in the same way as before
With iTPC one can go almost to zero transverse momentum
Ongoing work on BDT training, expecting improvement at low pT
𝒑 𝑻 = 0.0 – 0.1 GeV/c
Faculty of Nuclear Science and Physical Engineering

15. Faculty of Nuclear Science and Physical Engineering
Fixed target at 3.9 AGeV
Run 2019, fixed target, 𝐸 beam = 7.3 AGeV
Different geometry, different acceptance
Express production
MB events sampled: 2.8M
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16. Signal reconstruction, 𝒑 𝑻 and 𝒚 spectrum
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17. Raw yield and significance
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18. Faculty of Nuclear Science and Physical Engineering
Summary
KF Particle framework was introduced
Results from Λ reconstruction
Au+Au at 27 AGeV (2018) – KFPF + BDT
Au+Auat 14.6 AGeV (2019, express production)
Fixed target at 3.9 AGeV (2019, express production)
KF Particle gives promising results in lambda reconstruction at low 𝑝 𝑇 region, especially with BDT
KF Particle is more effective at low pT with respect to conventional analysis with topological cuts
Future work: multistrange baryons, BDT with iTPC data
Faculty of Nuclear Science and Physical Engineering

19. Faculty of Nuclear Science and Physical Engineering
References
KF Particle — S. Gorbunov, “On-line reconstruction algorithms for the CBM and ALICE experiments,” Dissertation thesis, Goethe University of Frankfurt, 2012,
KF Particle Finder — M. Zyzak, “Online selection of short-lived particles on many- core computer architectures in the CBM experiment at FAIR,” Dissertation thesis, Goethe University of Frankfurt, 2016, frankfurt.de/frontdoor/index/index/docId/41428
KF Particle Tutorial – M. Zyzak,
Strange particle measurements in Au+Au 27 GeV - Yue Hang Leng,
Analysis note for 𝐾 𝑆 0 , Λ, Λ , Ξ − , Ξ + production in Au+Au collisions at 𝑠 𝑁𝑁 = 7.7, 11.5, 19.6, 27 and 39 GeV – Xianglei Zhu,
Faculty of Nuclear Science and Physical Engineering

20. Thank you for your attention.
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21. Faculty of Nuclear Science and Physical Engineering
Backup slides
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22. Dataset, event selection, KFPF selection, 27 AGeV
Run 2018, Au+Au 27 GeV
Production P19ib, library SL19b
MB events sampled: 6.1⋅ 10 8
Events after selection: 3.7⋅ 10 8
𝑉 𝑧 <70 cm
More than 10% primary tracks
Default KFPF cuts
𝜒 𝑔𝑒𝑜𝑚 2 <10
𝑙/𝑑𝑙>5
𝑙>5 cm
𝜒 𝑝𝑟𝑖𝑚 2 >18.6
𝜒 𝑡𝑜𝑝𝑜 2 <5
𝑑 𝑚𝑎𝑥 <1 cm
Faculty of Nuclear Science and Physical Engineering

23. Faculty of Nuclear Science and Physical Engineering
Raw yield calculation
Fitting:
For 𝑝 𝑇 =0.2−0.4 GeV: double Gaussian with pol3 background
For 𝑝 𝑇 >0.4 GeV: double Gaussian with pol1 background
S = bin count in 3𝜎 window – integral of polynomial background
significance = 𝑆/ 𝑆+𝐵
Lowest transverse momentum bin: 𝑝 𝑇 = 0.2 – 0.4 GeV/c
Faculty of Nuclear Science and Physical Engineering

24. STAR Collaboration Meeting, Cracow, Poland
TMVA training example
TMVA plots for 𝒑 𝑻 =𝟎.𝟐−𝟎.𝟒 GeV
STAR Collaboration Meeting, Cracow, Poland

25. Significance scan example, 𝒑 𝑻 = 𝟎.𝟐 – 𝟐.𝟒 GeV/cB C
SG =749.53 B C
Default cuts A
SG = C B
STAR Collaboration Meeting, Cracow, Poland

26. STAR Collaboration Meeting, Cracow, Poland
TMVA vs. default cuts
𝒑 𝑻 = 0.1 – 0.2 GeV/c !
TMVA improves significance throughout whole 𝑝 𝑇 spectrum
Low pT enhancement up to 50%
Also enables reconstruction in very low 𝒑 𝑻 = 0.1 – 0.2 GeV/c
Default cuts
BDT application
STAR Collaboration Meeting, Cracow, Poland

27. Comparison with conventional analysis
BES I reconstruction of Λ
Dataset
AuAu, 27 GeV, 2011, P11id
72.8⋅ MB events
Lambdas at midrapidity, 𝑦 <0.5
For comparison extracted yields and errors from plots in analysis note “by hand”
Conventional analysis does not go under 𝑝 𝑇 =0.4 GeV/c
Analysis note -
Invariant mass plots -
STAR Collaboration Meeting, Cracow, Poland

28. Faculty of Nuclear Science and Physical Engineering
AuAu, 27 GeV, default cuts
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29. Faculty of Nuclear Science and Physical Engineering
Faculty of Nuclear Science and Physical Engineering

30. Faculty of Nuclear Science and Physical Engineering
AuAu, 27 GeV, BDT
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31. Faculty of Nuclear Science and Physical Engineering
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32. Faculty of Nuclear Science and Physical Engineering
AuAu, 14 GeV, default cuts
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33. Faculty of Nuclear Science and Physical Engineering
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Fxt, default cuts,
𝑬 𝒃𝒆𝒂𝒎 =𝟕.𝟑 GeV
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35. Faculty of Nuclear Science and Physical Engineering
Faculty of Nuclear Science and Physical Engineering