Machine learning approach for studying of dielectrons from open charm and beauty decays in p-Pb collisions with ALICE at the LHC

4 Nov 2019, 17:40
20m
Wanda Han Show Theatre & Wanda Reign Wuhan Hotel

Wanda Han Show Theatre & Wanda Reign Wuhan Hotel

Poster Presentation Electromagnetic probes Poster Session

Speaker

Dr Elisa Meninno (Stefan Meyer Institute for Subatomic Physics, Vienna)

Description

The measurement of low-mass $e^+e^-$ pairs is a powerful tool to study the properties of the Quark-Gluon Plasma (QGP) created in ultra-relativistic heavy-ion collisions. Since such pairs do not interact strongly and are emitted during all stages of the collisions, they provide information about the full time evolution and dynamics of the medium created.
Measurements in pp collisions are the necessary reference for heavy-ion studies. Dielectron production in p-Pb collisions can be used to investigate initial state effects, due to the presence of cold nuclear matter in the collision.
The main contribution to the dielectron continuum in the intermediate mass region 1.1 $<$ $\rm M_{\rm ee}$ $<$ 2.7 GeV/$c^2$ is coming from semi-leptonic decays of correlated beauty and charm hadrons.
In this poster, one possible way to study dielectrons from heavy-flavour hadron decays and to separate them from from other dielectron sources with the ALICE detector at LHC will be presented.
More explicitly, a machine learning approach based on Boosted Decision Tree (BDT) to isolate and study the contribution from heavy flavours will be explained.
The study will be reported based on the p-Pb collision data at $\sqrt{s_{\rm NN}} = 5.02$ TeV.

Primary author

Dr Elisa Meninno (Stefan Meyer Institute for Subatomic Physics, Vienna)

Presentation Materials