105 citations · 367 across the 30 of their papers we have counts for
13 papers · 1 filter
Posterior Inference of Hamiltonian Parameters from RIXS Spectroscopy
Samuel Klein, Thomas M. Linker, Louis Conreux +14
We present the first application of simulation-based inference to resonant inelastic X-ray scattering spectroscopy. Using truncated marginal neural ratio estimation to efficiently…
Unknown Unknowns: Model Misspecification in Machine Learning for Physics
Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held +1
Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the quest…
Machine-learned particle flow as a foundation model for collider physics
Farouk Mokhtar, Joosep Pata, Michael Kagan +1
The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representat…
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak +9
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics…
It Just Takes Two: Scaling Amortized Inference to Large Sets
Antoine Wehenkel, Michael Kagan, Lukas Heinrich +1
Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In many of these applications, the…
BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
Richard Hildebrandt, Evangelos Kourlitis, Baran Hashemi +7
We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engin…