Speaker
Fabricio Jimenez Morales
(LLR – CNRS, École polytechnique, Institut Polytechnique de Paris)
Description
The highly granular imaging calorimeters developed and operated by the CALICE collaboration provide a fertile testing ground for the application of innovative simulation and reconstruction techniques. Firstly, we show how granularity and the application of multivariate analysis algorithms enable the separation of close-by particles, and ParticleID. Secondly, we will outline how Machine Learning techniques are applied either to CALICE data to highlight shower structure quantitively, or to CALICE simulation framework for the generation of events, or to both to generate original – e.g. hardly measurable – samples from existing ones.
| In-person participation | Yes |
|---|
Primary author
Fabricio Jimenez Morales
(LLR – CNRS, École polytechnique, Institut Polytechnique de Paris)
