Parameterized neural networks for high-energy physics

Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Article number235
    JournalEuropean Physical Journal C
    Issue number5
    StatePublished - May 1 2016

    ASJC Scopus subject areas

    • Engineering (miscellaneous)
    • Physics and Astronomy (miscellaneous)


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