HEP ML Lab: An end-to-end framework for applying machine learning into phenomenology studies

Abstract Recent years have seen the development and growth of machine learning in high
energy physics. There will be more effort to continue exploring its full
potential. To make it easier for researchers to apply existing algorithms and
neural networks and to advance the reproducibility of the analysis, we develop
the {\sc HEP ML Lab} (\texttt{hml}), a Python-based, end-to-end framework for
phenomenology studies. It covers the complete workflow from event generation to
performance evaluation, and provides a consistent style of use for different
approaches. We propose an observable naming convention to streamline the data
extraction and conversion processes. In the {\sc Keras} style, we provide the
traditional cut-and-count and boosted decision trees together with neural
networks. We take the $W^+$ tagging as an example and evaluate all built-in
approaches with the metrics of significance and background rejection. With its
modular design, {\sc HEP ML Lab} is easy to extend and customize, and can be
used as a tool for both beginners and experienced researchers. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.