Explore the growing role of Python in High Energy Physics through this insightful PyCon US talk by Pratyush Das. Discover how Python has become the language of choice for physicists, second only to its adoption in Astrophysics. Learn about the historical context of computing in High Energy Physics and the pivotal role physicists played in computer science development. Examine the emergence of crucial libraries like cppyy and uproot, which have facilitated the transition from C++ to Python in the field. Gain an understanding of the Scikit-HEP project, a community-driven initiative providing a comprehensive ecosystem for data analysis in Particle Physics. Explore the interoperability between High Energy Physics tools and the broader scientific Python ecosystem. Delve into topics such as the Worldwide LHC Computing Grid, GPU acceleration, and the adoption of machine learning in physics research. Hear perspectives from practicing physicists and learn about Python's applications in other physics subfields. Conclude with insights into the future of Python in High Energy Physics and its potential to revolutionize the field.
Overview
Syllabus
Intro
What is High Energy Physics?
The first computers were built for Physics IEM
Computing Challenges in HEP today
Worldwide LHC Computing Grid
Is CPU power a solved problem?
Using GPUs to speed up computation IEM
Awkward Array
Requirements of a language to be used in HEP
Early history of Python in HEP
How do HEP physicists work with data?
What is ROOT?
ROOT and Python
Alternate implementation of ROOT in Python
uproot-Harbinger of Python in HEP?
Python is not so slow
Why use pybind11?
Scikit-HEP-overview of packages
Machine Learning
Hear from a Physicist
Python in other Physics
Adoption-existing HEP codebases
Concluding Remarks
Acknowledgements
Taught by
PyCon US
