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January 24, 2026Open Access

Unbinned Energy Correlator Unfolding in the context of top quark mass measurements

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Authors

SHSimon Hablas

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Overview

This work explores unbinned unfolding for accurate top quark mass determination using machine learning methods.

Key Points

  • This work aims to improve the measurement of the top quark mass using energy correlators through novel machine learning techniques.
  • Utilized generative machine learning methods for unbinned unfolding of energy correlators.
  • Implemented normalizing flows with conditional invertible neural networks and conditional flow matching.
  • Analyzed simulated data from boosted tt_bar events to enhance comparison with theoretical models.
  • Demonstrated the feasibility of applying machine learning methods for unfolding energy correlators.
  • Established a new observable to overcome the jet p_T dependency of the mass peak.
  • Showed promising results for model configurations with an observed mass bias towards m_t = 172.5 GeV.

Cite This Study

Simon Hablas (2026) studied this question.

synapsesocial.com/papers/697461a8bb9d90c67120b932https://doi.org/10.34726/hss.2026.137287
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