13-19 May 2018
Venice, Italy
Europe/Zurich timezone
The organisers warmly thank all participants for such a lively QM2018! See you in China in 2019!

Topological Cut Optimization for Lambda_c Reconstruction Using the Supervised Learning Algorithm in TMVA at STAR

15 May 2018, 17:00
2h 40m
First floor and third floor (Palazzo del Casinò)

First floor and third floor

Palazzo del Casinò

Poster Open heavy flavour Poster Session


Fu Chuan (Central China Normal University)


Measurement of charmed baryon, $\Lambda_c$, provides a unique tool to study the charm quark hadronization in the hot and dense medium created in heavy-ion collisions. With the dataset of Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV recorded by the STAR experiment at RHIC in 2014, $\Lambda_c$ signals were successfully reconstructed through the hadronic decay channel ($\Lambda_c\rightarrow pK\pi$) with a 5 $\sigma$ significance. Measurements of better precision of the $\Lambda_c$ production require more statistics and refined topological cut optimization.

In this poster, we will present $\Lambda_c$ reconstruction using the Toolkit for Multi-Variate Data Analysis (TMVA)-Boost Decision Tree (BDT) method with combined data from 2014 and 2016. The improvement in the signal significance is notable compared to previous results using the TMVA-Rectangular Cut Optimization method. We will also discuss the cut optimization for $\Lambda_c$ reconstruction in different transverse momentum and centrality bins with the TMVA-BDT method.

Collaboration STAR
Content type Experiment
Centralised submission by Collaboration Presenter name already specified

Primary author

Zhenyu Ye (University of Illinois at Chicago)

Presentation Materials

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