Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations

08/12/2022
by   Vincent Dumont, et al.
6

The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating events in High Energy Physics (HEP), including simulating detector responses and physics events. However, training GANs is notoriously hard and optimizing their hyperparameters even more so. It normally requires many trial-and-error training attempts to force a stable training and reach a reasonable fidelity. Significant tuning work has to be done to achieve the accuracy required by physics analyses. This work uses the physics-agnostic and high-performance-computer-friendly hyperparameter optimization tool HYPPO to optimize and examine the sensitivities of the hyperparameters of a GAN for two independent HEP datasets. This work provides the first insights into efficiently tuning GANs for Large Hadron Collider data. We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities. We also provide guidelines for how to go about GAN architecture tuning using the analysis tools in HYPPO.

READ FULL TEXT

page 2

page 4

page 5

page 6

page 12

page 17

page 18

page 19

research
01/20/2017

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

We provide a bridge between generative modeling in the Machine Learning ...
research
09/21/2020

On the Performance of Generative Adversarial Network (GAN) Variants: A Clinical Data Study

Generative Adversarial Network (GAN) is a useful type of Neural Networks...
research
09/26/2022

DVGAN: Stabilize Wasserstein GAN training for time-domain Gravitational Wave physics

Simulating time-domain observations of gravitational wave (GW) detector ...
research
05/28/2021

GAN for time series prediction, data assimilation and uncertainty quantification

We propose a new method in which a generative adversarial network (GAN) ...
research
04/26/2021

Efficient Hyperparameter Optimization for Physics-based Character Animation

Physics-based character animation has seen significant advances in recen...
research
06/25/2020

Ensembles of Generative Adversarial Networks for Disconnected Data

Most current computer vision datasets are composed of disconnected sets,...

Please sign up or login with your details

Forgot password? Click here to reset