28 February 2019 to 1 March 2019
Europe/Berlin timezone

Towards data-driven particle physics classifiers

1 Mar 2019, 12:10



Eric Metodiev (Massachusetts Institute of Technology)


Deep learning in particle physics often relies on imperfect simulations due to the lack of real labelled data, which risks learning mismodeling artifacts rather than the underlying physics. In this talk, I discuss the prospects for training classifiers directly on collider data using mixed samples, drawing from techniques in weak supervision and topic modeling. Using the example of quark versus gluon jet classification, I demonstrate how these ideas allow data-driven classifiers to be trained and actually provide an operational definition of the underlying categories.

Primary author

Eric Metodiev (Massachusetts Institute of Technology)


Jesse Thaler (MIT) Patrick Komiske (Massachusetts Institute of Technology)

Presentation Materials

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