FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Reconstructing the Kinematics of Deep Inelastic Scattering with Deep Learning
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abstract
We introduce a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance. The method is studied with simulated events at HERA and the future Electron-Ion Collider (EIC). We show that the DNN method outperforms all the traditional methods over the full phase space, improving resolution and reducing bias. Our method has the potential to extend the kinematic reach of future experiments at the EIC, and thus their discovery potential in polarized and nuclear DIS.
years
2026 2representative citing papers
The 2026 DPHEP report records substantial progress in HEP data preservation, including modern reanalyses of LEP legacy data and expanding open-data policies, alongside sustainability challenges.
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Data Preservation in High Energy Physics: Global Report 2026
The 2026 DPHEP report records substantial progress in HEP data preservation, including modern reanalyses of LEP legacy data and expanding open-data policies, alongside sustainability challenges.