2nd IML Machine Learning Workshop

Classification of decays involving variable decay chains with convolutional architectures

11 Apr 2018, 16:30

20m

500/1-001 - Main Auditorium (CERN)

Session 6

Speaker

Justin Tan (The University of Melbourne, Belle II)

Description

Vidyo contribution

We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II detector.

Presentation materials

iml_conv_final.pdf

Recording