In 2027 CERN is expected to start the High-Luminosity LHC (HL-LHC) phase. HL-LHC will integrate 10 times the current luminosity, leading to a high pile-up rate and unprecedented radiation levels. In order to cope with such a harsh environment and maintain the current physics performance, a major upgrade of the LHC detectors is required. As part of the HL-LHC detector upgrade programme, the CMS experiment is developing a High Granularity Calorimeter (HGCAL) to replace the existing endcap calorimeters.
Beam tests play a fundamental role in the validation of the detector design and in the study of its physics performance. In a typical beam test environment, it is important to have quick access to data, in order to perform explorative analysis, data visualization for the main physical distributions and to run data quality monitoring. During the offline analysis it is very often necessary to run comparisons between data and simulations, to identify problematic features in data, to develop preselection and cleaning cuts, and to reconstruct different observables distributions to produce the final results by means of statistical analysis.
Jupyter Notebooks are being used more and more by physicists to face this kind of tasks, as they provide an interactive interface where code can be executed, documented, and the outputs can be directly produced, analyzed and visualised. The data can be access remotely without the need of downloading to a local machine.
After a brief introduction about the HGCAL, the talk will focus on the major benefits that come from the use of interactive notebooks during a test beam campaign and in the subsequent phase of data analysis.