CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

By Michela Paganini, Luke de Oliveira, Benjamin Nachman. 2017

359 citations recorded by the source corpus.

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce CaloGAN, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter and achieve speedup factors comparable to or better than existing full simulation techniques on CPU ($100\ifmmode\times\else\texttimes\fi{}--1000\ifmmode\times\else\texttimes\fi{}$) and even faster on GPU (up to $\ensuremath{\sim}{10}^{5}\ifmmode\times\else\texttimes\fi{}$). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons, and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.

Subfields: Nuclear and High Energy Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “, the design of scientific work-flows [83], [84] or the generation of model simulations [85].”
Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning (2017, neutral): “ generator / data) or from the generator network. These two networks ‘compete’ and when the discriminator is confused, the generator will be a 103 good simulator. This method has been studied in Ref. [591,592,593,594,595] for calorimeter simulations of individual hadrons. An alternative to the GAN i”
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey (2019, background): “…et al. 2017), 3D object generation (Wu et al. 2016a) [for instance dental restoration (Hwang et al. 2018)], music generation (Yang et al. 2017), scientific simulations acceleration [for example in astrophysics (Mustafa et al. 2017) or high energy physics (Paganini et al. 2018)] and many more.”
How Generative Adversarial Networks and Its Variants Work : An Overview of GAN (2017, methodology): “By estimating real data probability distribution pdata(x) where x exists in the d-dimensional real space R, generative models can be applied to various tasks such as semi-supervised learning [97], [22],[64], image translation [52],[56], [11], domain adaptation [2],[13],[100] and other academic domai”