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arXiv · 2204.11889

Boosting mono-jet searches with model-agnostic machine learning

Thorben Finke·Michael Krämer·Maximilian Lipp·Alexander Mück
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Abstract

We show how weakly supervised machine learning can improve the sensitivity of LHC mono-jet searches to new physics models with anomalous jet dynamics. The Classification Without Labels (CWoLa) method is used to extract all the information available from low-level detector information without any reference to specific new physics models. For the example of a strongly interacting dark matter model, we employ simulated data to show that the discovery potential of an existing generic search can be boosted considerably.

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Thorben Finke, Michael Krämer, Maximilian Lipp, Alexander Mück. 2022-04-25. Boosting mono-jet searches with model-agnostic machine learning. https://doi.org/10.1007/jhep08(2022)015

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