# Quark Matter 2018

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

### Speaker

Fu Chuan (Central China Normal University)

### Description

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 Experiment Presenter name already specified

### Primary author

Zhenyu Ye (University of Illinois at Chicago)