Nuclear Physics Seminar

"Machine learning based reconstruction of jet transverse momentum in ALICE"

Presented by Hannah Bossi, Yale

Tuesday, June 16, 2020, 11:00 am —

Jets in heavy-ion collisions are expected to lose energy through strong interactions with the QGP medium, a phenomena referred to as jet quenching. Experimental observations of jet quenching through observables such as the nuclear modification factor ($R_{\rm AA}$) rely on the precise reconstruction of jet transverse momentum ($p_{\rm T}$). However, jet $p_{\rm T}$ reconstruction is made difficult in heavy-ion collisions due to the large fluctuating background from the underlying event. One common treatment of this background is to perform a pedestal subtraction of the event-averaged momentum density. While this method effectively corrects for the average background, it does not account for region-to-region fluctuations. A novel method to correct the jet $p_{\rm T}$ using machine learning (ML) techniques will be presented. This method utilizes ML techniques to reconstruct the jet $p_{\rm T}$ from jet parameters, including the constituents of the jet. With an ML-based correction, residual fluctuations are reduced, allowing for measurements of the nuclear modification factor at lower $p_{\rm T}$ and larger jet radii (\textit{R}) than previously possible in ALICE. Extending measurements to these regions could help disentangle competing mechanisms of jet energy loss and the recovery of said energy due to the response of the medium. Studies that investigate and estimate the potential fragmentation bias of this ML approach will also be presented. Keeping such studies in mind, comparisons to models will be shown, with the aim of constraining such models at the previously unexplored region of lower $p_{\rm T}$.

Hosted by: Alba Soto Ontoso

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