Search Results for author:
Found 7 papers, 3 papers with code
Deep Generative Models for Detector Signature Simulation: An Analytical Taxonomy
Signatures from particle physics detectors are low-level objects encoding the physics of collisions.
Inductive Simulation of Calorimeter Showers with Normalizing Flows
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline.
CaloFlow for CaloChallenge Dataset 1
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
New directions for surrogate models and differentiable programming for High Energy Physics detector simulation
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
i-flow: High-dimensional Integration and Sampling with Normalizing Flows
We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows.
