Differentiable MadNIS-Lite
arXiv:2408.01486 · doi:10.21468/SciPostPhys.18.1.017
Abstract
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MadNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MadNIS.
16 pages, 6 figures
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Cited by in corpus (10)
- A Lorentz-Equivariant Transformer for All of the LHC
- Accurate Surrogate Amplitudes with Calibrated Uncertainties
- Sampling NNLO QCD phase space with normalizing flows
- Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
- BitHEP -- The Limits of Low-Precision ML in HEP
- Amplitude Uncertainties Everywhere All at Once
- Extrapolating Jet Radiation with Autoregressive Transformers
- How to Unfold Top Decays
- Amplitude Surrogates for Multi-Jet Processes
- NNLO QCD corrections to from Local Unitarity combined with Coulomb resummation and NLO EW effects