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J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning
Original
1902
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H. Qu and L. Gouskos, ParticleNet: Jet Tagging via Particle Clouds
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1902
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A. Dey, J. Lahiri, and B. Mukhopadhyaya, LHC signals of a heavy doublet Higgs as dark matter portal: cut-based approach and improvement with gradient boosting and neural networks
Original
1905
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E. Bernreuther, F. Kahlhoefer, M. Krämer, and P. Tunney, Strongly interacting dark sectors in the early Universe and at the LHC through a simplified portal
Original
1907
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C. K. Khosa, V. Sanz, and M. Soughton, Using machine learning to disentangle LHC signatures of Dark Matter candidates
Original
1910
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Original
2001
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A. Andreassen, B. Nachman, and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection
Original
2001
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2005
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ATLAS Collaboration
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2005
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2006
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K. Benkendorfer, L. L. Pottier, and B. Nachman, Simulation-assisted decorrelation for resonant anomaly detection
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2009
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N. D. Christensen and C. Duhr, FeynRules - Feynman rules made easy
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2009
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L. Carloni and T. Sjostrand, Visible Effects of Invisible Hidden Valley Radiation
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2010
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2011
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Original
2012
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2022

Boosting mono-jet searches with model-agnostic machine learning

Finke, Thorben, Krämer, Michael, Lipp, Maximilian et al.

Understand

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.