Abstract
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 \textsc{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 ($100\times$-$1000\times$) and even faster on GPU (up to $\sim10^5\times$). 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.
Highlights
The physics programs of all experiments based at the Large Hadron Collider (LHC) rely heavily on detailed simulation for all aspects of event reconstruction and data analysis
The game-theoretical basis for this framework [40,41] ensures that if we extend the space of allowed functions that G and D can draw from to be the space of all continuous functions, there exists some G that exactly recovers the target distribution f, i.e., g → f, while for every sample produced by the generator, the discriminator is maximally confused and admits a posterior of being real of 1⁄2
We identify this solution with the name CALOGAN
Summary
The physics programs of all experiments based at the LHC rely heavily on detailed simulation for all aspects of event reconstruction and data analysis. State-of-the-art simulations are able to precisely model detector geometries and physical processes spanning distance scales as small as 10−20 m for the initial parton-parton scattering, all the way to the material interactions at meter length scales These processes, which include nuclear and atomic interactions, such as ionization, as well as strong, weak, and electromagnetic processes, will alter the state of incoming particles as they propagate through and interact with layers of material in the various detector components. The ATLAS and CMS experiments at the highluminosity phase of the LHC (HL-LHC) will each see about three billion top quark pair events [4,5,6,7,8,9,10]; for a MC statistical uncertainty that is significantly below the data uncertainty, hundreds of billion simulated events would be required This is not possible using full detector simulation techniques with existing computing resources.
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