Speaker
Kinga Anna Wozniak
(University of Vienna (AT))
Description
We propose a new search strategy, based on deep-learning (DL) anomaly detection, to search for new physics in all-jet final states without specific assumptions. The DL model identifies events with anomalous radiation pattern in the jets. This is done applying a threshold to the reconstruction loss. The threshold is tuned so that the rejected events provide an estimate of the QCD-background distribution of analysis-specific interesting quantities. The method can be generalized to many final states without re-training the model and allows to determine the presence of a new-physics signal without making specific assumptions on the signal shape.
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Primary authors
Javier Mauricio Duarte
(Fermi National Accelerator Lab. (US))
Maurizio Pierini
(CERN)
Kinga Anna Wozniak
(University of Vienna (AT))
Jennifer Ngadiuba
(CERN)
Eric Moreno
(California Institute of Technology)
Prof.
Maria Spiropulu
(California Institute of Technology)
Dr
Jean-Roch Vlimant
(California Institute of Technology (US))
Olmo Cerri
(California Institute of Technology (US))
Thong Nguyen
(California Institute of Technology (US))
