489 citations · 3.7k across the 37 of their papers we have counts for
7 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…
A Roadmap for HEP Software and Computing R&D for the 2020s
Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio +307
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facil…
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
Wahid Bhimji, Steven Andrew Farrell, Thorsten Kurth +3
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely f…
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…
Machine Learning Algorithms for -Jet Tagging at the ATLAS Experiment
Michela Paganini
The separation of -quark initiated jets from those coming from lighter quark flavors (-tagging) is a fundamental tool for the ATLAS physics program at the CERN Large Hadron C…
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…