Nils Krumnack

In the ATLAS analysis model, users interact with specialized algorithms to perform a variety of tasks on their physics objects including calibration, identification, and obtaining systematic uncertainties for simulated events. These algorithms have a wide variety of configurations, and often must be applied in specific orders. A user-friendly configuration mechanism has been developed with the goal of improving the user experience from the perspective of both ease-of-use and stability. Users can now configure necessary algorithms via a YAML file, enabled by a physics-oriented python configuration. The configuration mechanism and training are discussed.§

The ATLAS experiment at CERN explores vast amounts of physics data to answer the most fundamental questions of the Universe. The prevalence of Python in scientific computing motivated ATLAS to adopt it for its data analysis workflows while enhancing users’ experience. This paper will describe to a broad audience how a large scientific collaboration leverages the power of the Scientific Python ecosystem to tackle domain-specific challenges and advance our understanding of the Cosmos. Through a simplified example of the renowned Higgs boson discovery, attendees will gain insights into the utilization of Python libraries to discriminate a signal in immersive noise, through tasks such as data cleaning, feature engineering, statistical interpretation and visualization at scale.