Parameterized Machine Learning for High-Energy Physics

28 Jan 2016Pierre BaldiKyle CranmerTaylor FaucettPeter SadowskiDaniel Whiteson

We investigate a new structure for machine learning classifiers 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... (read more)

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