Speaker
Description
Reconstructing the jet transverse momentum ($p_{\rm T}$)is a challenging task, particularly in heavy-ion collisions due to the large fluctuating background from the underlying event. In the recent years, ALICE has developed a novel method to correct jets for this large background using machine learning techniques. This analysis intentionally does not utilize deep learning methods and instead utilizes a shallow neural network for simplicity. This approach uses jet properties, including the constituents of the jet, to create a mapping between the corrected and uncorrected jet $p_{\rm T}$ In comparison to the standard ALICE method, this machine learning based estimator demonstrates a significantly improved performance. The ML estimator was then applied to data in order to perform a measurement of full jets (jets containing both charged and neutral constituents) to lower transverse momenta than previously possible in ALICE. An ongoing challenge facing ML for jet physics is the interpretation of results and understanding potential biases. In this particular result, including constituent information in training introduces a bias towards PYTHIA-like fragmentation patterns, which has been shown to differ from the fragmentation measured in Pb--Pb data. Recent studies focusing on this bias in an attempt to further investigate and quantify its impact will be shown.
| Affiliation | Yale University |
|---|---|
| Academic Rank | PhD student |
