• DOI:10.22323/1.398.0302
• Corpus ID: 244773227

Jet tomography in hot QCD medium with deep learning

@article{Du2021JetTI,
  title={Jet tomography in hot QCD medium with deep learning},
  author={Yilun Du and Daniel Pablos and Konrad Tywoniuk},
  journal={Proceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2021)},
  year={2021}
}

• Y. Du, D. Pablos, K. Tywoniuk
• Published 1 December 2021
• Physics
• Proceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2021)

With deep learning techniques, the degree of modification of energetic jets that traversed hot QCD medium can be identified on a jet-by-jet basis. Due to the strong correlations between the degree of jet modification and its traversed length in the medium, we demonstrate the power of our novel method to locate the creation point of a dijet pair in the nuclear overlap region. In particular, jet properties, such as jet width and orientation can serve as additional handles to locate the creation points to a higher level of precision, which constitutes a significant development towards the long-standing goal of using jets as tomographic probes of the quark-gluon plasma.