July 29, 2019 to August 2, 2019
Northeastern University
US/Eastern timezone

Determination of CMS Barrel Test Beam Calorimeter Reponse Correction to Pion Beams with Convolutional Neural Networks

Jul 31, 2019, 5:00 PM
Shillman 425 (Northeastern University)

Shillman 425

Northeastern University

Oral Presentation Computing, Analysis Tools, & Data Handling Computing, Analysis Tools, & Data Handling


Daniel Li (Brown University (US))


We investigate modern machine learning techniques to derive calibration for the combined CMS electromagnetic and hadronic calorimeter system. We use the dataset from a 2006 CMS test beam to measure the calorimeter responses to pion beams of various energies. The performance of the network is evaluated by studying the linearity of calibrated responses. A convolutional neural network approach is used to train on a range of beam momenta from $2$ to $200\ GeV/c$ and to apply the correction to the energy distribution.

Primary author

Daniel Li (Brown University (US))


Sergei Gleyzer (University of Florida (US)) Meenakshi Narain (Brown University (US)) Ulrich Heintz (Brown University (US)) Sitong An (CERN, Carnegie Mellon University (US)) Jason Terry (Brown University) Andrew Dabydeen (Brown University) Emanuele Usai (Brown University (US))

Presentation materials