Speaker
Description
The ROOT software framework is widely used from Python in HEP for storage, processing, analysis and visualization of large datasets. With the large increase in usage of ML from the Python ecosystem for experiment workflows, especially lately in the last steps of the analysis pipeline, the matter of exposing ROOT data ergonomically to ML models becomes ever more pressing. In this contribution we discuss the experimental component of ROOT that exposes ROOT datasets in batches ready for the training phase. A new shuffling strategy for creating the batches to prevent biased training is discussed, taking as examples real-life use cases relative to ATLAS Open Data.
An end-to-end ML physics analysis using ATLAS Open Data is carried out to show how training a model with common ML tools can be done directly from ROOT datasets to avoid intermediate data conversions, streamline workflows and used in the case where the training data does not fit in memory.
