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2021

E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once

Nachman, Benjamin, Thaler, Jesse

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There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature.

To evaluate the efficacy of these proposals, we study the connection between single-event classifiers and multi-event classifiers under the assumption that collider events are independent and identically distributed (IID).
We show how one can build optimal multi-event classifiers from single-event classifiers, and we also show how to construct multi-event classifiers such that they produce optimal single-event classifiers.
This is illustrated for a Gaussian example as well as for classification tasks relevant for searches and measurements at the Large Hadron Collider.

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