Boosting mono-jet searches with model-agnostic machine learning [article]

Thorben Finke, Michael Krämer, Maximilian Lipp, Alexander Mück
2022 arXiv   pre-print
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.
arXiv:2204.11889v2 fatcat:idgqdt4yy5bmtipyyikdogfnk4