【444】 Machine Learning for the ALICE Upgrade: Performance Enhancement of Dilepton Analyses

23 Aug 2017, 12:30
1h 30m
Poster Nuclear, Particle- and Astrophysics (TASK - FAKT) Poster Session

Speaker

Sebastian Templ (Austrian Academy of Sciences (AT))

Description

ALICE, the dedicated heavy-ion experiment at CERN—LHC, will undergo a major upgrade in 2019/20. In this work, we analyze low-mass dielectrons in Pb—Pb collisions after this upgrade. These $e^+e^-$ pairs are, for example, sensitive to the temperature of the collision system. Due to their small signal-to-background ratio, high-purity dielectron samples are required, which can be provided by traditional, cut-based analyses, however at the price of low signal efficiency. We aim to improve on existing methods by employing a multivariate approach to reject combinatorial background, conversion pairs and heavy-flavor contributions to the dielectron spectrum in a scenario involving the planned upgrade of the ALICE Inner Tracking System.

Author

Sebastian Templ (Austrian Academy of Sciences (AT))

Presentation materials

There are no materials yet.