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Machine learning has proven to be an indispensable tool in the selection ofinteresting events in high energy physics. Such technologies will becomeincreasingly important as detector upgrades are introduced and data ratesincrease by orders of magnitude. We propose a toolkit to enable the creation ofa drone classifier from any machine learning classifier, such that differentclassifiers may be standardised into a single form and executed in parallel. Wedemonstrate the capability of the drone neural network to learn the requiredproperties of the input neural network without the use of any labels from thetraining data, only using appropriate questioning of the input neural network.

NNDrone: a toolkit for the mass application of machine learning in High Energy Physics