Talk given as part of SNOLAB's 2023 seminar series on September 18th, 2023.
Abstract
As the High-Luminosity Large Hadron Collider (HL-LHC) era looms on the horizon for physics analysis at the LHC experiments there are multiple challenges to be overcome to efficiently analyze and extract the most scientific value out of these unique and valuable data. In this seminar I will present a high-level overview of how applications of data science tools from the modern scientific open source community, techniques and applications from the field of automatic differentiation, and strategies for enabling analysis reuse can be leveraged to achieve robust data analysis workflows at the HL-LHC scale. I will also offer proposals for how these tools and techniques can be applied beyond the particle physics community to the broader scientific computing landscape.
Viewable online here
Example code runnable on Binder:
- Matthew Feickert is supported in part by IRIS-HEP
