Speaker
Ruediger Haake
(Yale University)
Description
The precise reconstruction of jet transverse momenta in heavy-ion collisions is a challenging task. A major obstacle is the large number of (mainly) low-$p_\mathrm{T}$ particles overlaying the jets. Strong region-to-region fluctuations of this background complicate the jet measurement and lead to significant uncertainties.
In this talk, a novel approach to correct jet momenta (or energies) for the underlying background in heavy-ion collisions will be presented for the first time. The proposed method was recently described in a paper submitted to PRC[1]. The analysis makes use of common Machine Learning techniques to estimate the jet transverse momentum based on several parameters, including properties of the jet constituents. Using a toy model and HIJING simulations, the performance of the new method is shown to be superior to the established standard area-based background estimator. The application of the new method to data promises the measurement of jets down to extremely low transverse momenta, unprecedented thus far in data on heavy-ion collisions.
[1] preprint available at https://arxiv.org/abs/1810.06324
Primary author
Ruediger Haake
(Yale University)
