Speaker
Luca Marco Lavezzo
(MIT)
Description
We explore a possible avenue for detecting Dark Showers that manifest as Soft Unclustered Energy Patterns (SUEP) in the detector with the use of supervised machine learning techniques and transfer learning. We employ a ResNet model based on Convolutional Neural Networks (CNNs) to classify events. Additionally, a robust, data-driven background estimation technique is embedded into the model architecture through a Distance Correlation (DiSco) term in the loss function of the network; this achieves decorrelation between the classifier output and another physics-motivated discriminant in order to estimate background in the signal region through the ABCD method.
Authors
Chad Wells Freer
(Massachusetts Inst. of Technology (US))
Luca Marco Lavezzo
(MIT)
Benedikt Maier
(CERN)
Adrian Alan Pol
(Princeton University (US))
Isobel Ojalvo
(Princeton University (US))
Christoph Paus
(Massachusetts Inst. of Technology (US))
Maurizio Pierini
(CERN)
