Strangeness production in Au+Au collisions at 27 GeV at STAR

Yue Hang Leung, for the STAR CollaborationLawrence Berkeley National Laboratory

hadrons at 200 GeV

The STAR Collaboration https://drupal.star.bnl.gov/STAR/presentations

Abstract Strangeness production is a classic tool to study properties of the quark gluon plasma (QGP). The ratios of particle yields involving strange particles are often utilized to study various properties of nuclear matter, such as the strangeness chemical potential and the chemical freeze-out temperature. In particular, the yield ratios , are suggested in [1] to be sensitive to strange quark density fluctuations. Studying this ratio as a function of collision energy may provide a unique probe to the fluctuation of strange quark densities during the phase transition from the QGP to hadronic matter. In addition, studying the central-to-peripheral nuclear modification factor of strange hadrons may provide insight on strangeness production mechanisms in nuclear collisions.

In this poster, we present new measurements of mid-rapidity strange baryons from 27 Au+Au collisions at RHIC. The large data sample taken in 2018 by STAR and the use of supervised machine learning techniques extend both the low and high reach compared to previous measurements. We present the yield ratio , and discuss their physics implications.

Motivation

[1] C. M. Ko, EPJ Web of Conferences 171, 03002 (2018)

[2] K.J. Sun et al., Phys. Lett. B 774, 103 (2017)

[3] M. Zyzak, “Online selection of short-lived particles on many-core computer architectures in aaaaaathe CBM experiment at FAIR”, thesis, urn:nbn:de:hebis:30:3-414288 [4] L. Adamczyk et al (STAR collaboration), Phys. Rev. C 93, 021903 (2016)

Reference

o Particle yields may carry information on the properties of QGP and onset of deconfinement.

o The yield ratio maybe sensitive to the neutron relative density fluctuation at kinetic freezeout[2].

o Non-monotonic behavior observed in Au+Au 0-10%. o Similarly strange quark relative density fluctuation may be probed by the

ratio .

o Although such yields have been measured using BES-I data, o Large systematic uncertainties from low pT extrapolation. o BES-II provides x10 statistics. o Apply supervised learning techniques to optimize significance and

improve low pT reach.

NKNΞ/NϕNΛ NKNΩ/NϕNΞ

RCP

pTΛ(Λ̄), Ξ(Ξ̄), Ω(Ω̄)

NKNΞ/NϕNΛ

NtNp/N2d

Δn = ⟨(δn)2⟩/⟨n⟩2

NKNΞ/NϕNΛ

Particle reconstruction: KFparticle methodo Kalman Filter based reconstruction method; developed based on CBM, ALICE

experiments [3]. o All particles (mother and daughter) described by state vectors and covariance

matrices. o Reconstruction of decay chains and

computation of all physics parameters and associated uncertainties.

o Analysis cuts based on physical parameters normalized by their respective error.

Cut optimization: TMVA (Toolkit for Multivariate Data Analysis) )o A family of supervised learning algorithms

o make use of training events, typically events labelled “signal” or “background”.

o Multivariate analysis that takes into account correlations between different variables.

o Training on multiple variables (decay length, DCA, etc.) based on the BDT (Boosted decision tree) method.

o All variables simplified to a single axis (BDT response)

o Scan BDT response cut value to optimize signal significance.

Mon Oct 14 19:34:46 2019 BDT response0.3− 0.2− 0.1− 0 0.1 0.2 0.3

dx / (1

/N) d

N

0

1

2

3

4

5

6 Signal (test sample)Background (test sample)

Signal (training sample)Background (training sample)

Kolmogorov-Smirnov test: signal (background) probability = 0.014 (0.029)

U/O

-flow

(S,B

): (0

.0, 0

.0)%

/ (0

.0, 0

.0)%

TMVA overtraining check for classifier: BDT

Signal: embedding simulation Background: sideband from real data

Λ, Ξ, Ω ReconstructionΛ, Ξ, Ωo reconstructed through their hadronic decay channels.

Decay channel Branching ratio

63.9%

99.987%

67.8%

Λ(Λ̄)Ξ(Ξ̄)Ω(Ω̄)

p(p̄) + π−(π+)Λ(Λ̄) + π−(π+)

Λ(Λ̄) + K−(K+)

Signal extractiono Raw counts and significance calculated within a 3 sigma mass range with

the bin counting method. o Background modeled by polynomial fitting.o Comparing to traditional reconstruction method, the KFparticle+TMVA

method increases the significance by ~40% for and ~80% for at 27 GeV.Λ Ω

0.5 1 1.5 2 2.5 [GeV/c]

Tp

0

0.5

1

1.5

2

2.5

sign

ifica

nce

ratio

Au + Au, sNN = 27GeV

Ω−

0 − 5 % centrality

10 210 [GeV]NNs

0

0.5

1

1.5

2

2.5Rat

io

Λ Nφ / N-Ξ N+KN

Λ Nφ / N+

Ξ N-KN

0-10% centrality

o The ratio for the 0-10% Au+Au collisions constructed using previous measurements shows a decreasing trend.

o Future BES-II data at lower energies will be useful to probe potential strange quark fluctuations at the QGP to hadronic matter phase transition, and in general enhance our understanding of strangeness production mechanisms and nuclear matter.

Summary o Applied TMVA-BDT method to strange particle reconstruction, achieved increase in

the signal significance by ~40% for and ~80% for in Au+Au collisions at 27 GeV.

ΩΛ

o Expect significantly improved statistical significance using 2018 data with new analysis methods compared to previous measurements [4].

sNN =

sNN =

1.64 1.66 1.68 1.7 1.72]2 [GeV/c-KΛm

0

500

1000C

ount

s FGcomb BGFG-comb BG

-Ω= 0.8 - 1.2[GeV/c]

Tp

0-5% centrality/NDF=41.7/322χ

]2mean=1.672 [GeV/c]2std dev=1.820 [MeV/c

Traditional reconstructionAu + Au, sNN = 27GeV

1.64 1.66 1.68 1.7 1.72]2 [GeV/c-KΛm

0

500

1000

1500

2000

Cou

nts FG

BG -Ω= 1.2 - 1.6[GeV/c]

Tp

0-5% centrality/NDF=49.8/502χ

]2mean=1.672 [GeV/c]2std dev=1.908 [MeV/c

Au + Au, sNN = 27GeV

KFparticle+TMVASignificance(KFparticle + TMVA)

Signifiance(traditional1reco.)

NKNΞ/NϕNΛ

o Measurements using new analysis methods and data can help reduce the uncertainty of such ratios.

sNN =

In part supported by

0.5 1 1.5 2 2.5 3 3.5[GeV/c]

Tp

0.8

0.9

1

1.1

1.2R

atio

to 2

011

data =27 GeVNNs, Au+Au Ω

2018 projection, 0-10%2011, 0-10%

Statistical projections and outlook