Speaker
Description
We present quantum tomography (QT) as a new framework for uncovering the internal structure of hadrons in high-energy collisions. Inspired by techniques from quantum state reconstruction, QT provides a data-driven approach for reconstructing higher-dimensional features of hadronic structure directly from lower-dimensional experimental data, without reliance on specific models.
We illustrate this framework through applications to ultra-peripheral collisions (UPCs) and processes at the Electron-Ion Collider (EIC), showing how QT can access 3D spatial and momentum distributions in exclusive and semi-inclusive reactions.
Building on these foundations and examples in DIS-related measurements, we introduce AI-driven extensions enabling it to tackle the large, complex datasets emerging from modern facilities like the LHC and EIC.
This work opens a new pathway for fundamental physics to uncover the hidden multidimensional structure of matter directly from experimental measurements.
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