375 citations · 1.3k across the 8 of their papers we have counts for
4 papers · 1 filter
CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
Michela Paganini, Luke de Oliveira, Benjamin Nachman
The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measu…
Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters
Luke de Oliveira, Michela Paganini, Benjamin Nachman
High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics. Most…
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Michela Paganini, Luke de Oliveira, Benjamin Nachman
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of particle collisions to build expectations of what experimental data may look like under different theo…
Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
Luke de Oliveira, Michela Paganini, Benjamin Nachman
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Ad…