154 citations · 168 across the 3 of their papers we have counts for
5 papers
Discriminative versus Generative Approaches to Simulation-based Inference
Benjamin Sluijter, Sascha Diefenbacher, Wahid Bhimji +1
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate…
Refining Fast Calorimeter Simulations with a Schrödinger Bridge
Sascha Diefenbacher, Vinicius Mikuni, Benjamin Nachman
Machine learning-based simulations, especially calorimeter simulations, are promising tools for approximating the precision of classical high energy physics simulations with a frac…
New Angles on Fast Calorimeter Shower Simulation
Sascha Diefenbacher, Engin Eren, Frank Gaede +5
The demands placed on computational resources by the simulation requirements of high energy physics experiments motivate the development of novel simulation tools. Machine learning…
Hadrons, Better, Faster, Stronger
Erik Buhmann, Sascha Diefenbacher, Engin Eren +7
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-lea…
Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
Erik Buhmann, Sascha Diefenbacher, Engin Eren +4
Accurate simulation of physical processes is crucial for the success of modern particle physics. However, simulating the development and interaction of particle showers with calori…