Reliably simulating detector response to hadrons is crucial for almost all physics programs at the Large Hadron Collider. The core component of such simulation is the modeling of hadronic interactions. Unfortunately, there is no first-principle theory guidance. The current state-of-the-art simulation tool, Geant4, exploits phenomenology-inspired parametric models, each simulating a specific range of hadron energies for some hadron flavor types. These models must be combined to simulate all hadron flavors at all energy ranges. Parameters in each model and the transition region between models must be tuned to match the experimental measurements. Those models may be updated to cope with new measurements. Our work is to make the modeling of hadronic interactions differentiable so that it is easy to tune and to unify all parametric models with one machine learning-based model so that it is easy to maintain and update. To this end, we exploit the conditional normalizing flow models and train them with simulated data. Our work is the first step toward developing a fully differentiable and data-driven simulation model for hadronic interactions for High Energy and Nuclear Physics.
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