Parameterized neural networks for high-energy physics

The European Physical Journal C

Pierre Baldi¹  Peter Sadowski¹  Taylor Faucett²  Daniel Whiteson²  Kyle Cranmer³ 

[1] Department of Computer Science, University of California, 92697, Irvine, CA, USA;Department of Physics and Astronomy, University of California, 92697, Irvine, CA, USA;Department of Physics, NYU, New York, NY, USA;

关键词: Parameterized Network;    Nuisance Parameter;    Deep Neural Network;    Single Network;    Stochastic Gradient Descent;   

DOI  :  10.1140/epjc/s10052-016-4099-4

来源: Springer

【 摘 要 】

We investigate a new structure for machine learning classifiers built with neural networks and applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results.

【 授权许可】

CC BY