Automatic fast monitoring of LHC data

Exploring generic experimental signatures with TADA, a monitoring tool for CERN's ATLAS experiment at the Large Hadron Collider (LHC).

The TAg DAta (TADA) system was developed as part of the ATLAS experiment at CERN's Large Hadron Collider (LHC). It served as a fast monitoring system designed to process and analyze data from particle collisions. TADA simplified the massive volume of raw data produced by ATLAS, organizing it into more manageable formats for real-time analysis and offline validation. Its goal was to ensure the quality of recorded data and identify unusual patterns that serve as a validation for simulation and data quality, as well as a tool for hinting at potential anomalies in the data.

During my doctoral research, I worked on enhancing TADA's functionality to handle complex data monitoring challenges, targeting generic experimental signatures. This involved integrating automatic selection criteria, filtering and analysis of particle data. I leveraged Python and C++ for the processing of large datasets, expanding TADA's scope to monitor several observables for hundreds of decay channels.

TADA would automatically produce a webpage with plots with recent data and Monte Carlo simulations for critical observables like invariant mass and transverse momentum. You can read more about it in my doctoral thesis (Chapter 4).