Julia is a modern programming language designed for high-performance scientific computing, offering the ease of dynamic languages with the efficiency of low-level languages. This hands-on session introduces Julia to scientists with prior programming experience (e.g. Python, C++, Fortran), focusing on its unique features such as multiple dispatch, just-in-time (JIT) compilation, and efficient...
Part 1: Motivation and Basic analysis setup
We introduce basic data operation and JuliaHEP analysis setup by going through the follow topics:
- use Open Data
- implement cut and count
- simple visualization
- write to Arrow or Parquet prepare for ML
Part 2: Basic ML and intro to MLJ
We demonstrate how to conduct the most common types of HEP analysis ML tasks in Julia,...
Julia has a rich statistics ecosystem, but when it comes to the type of inference typical for high energy physics and related fields, some gaps still remain. Still, a lot of progress has been made - where are we, and where to we go next?
There are two main kinds of “fitting” in HEP – fitting function to data, and template fitting for statistical analysis. The two are linked, but each has enough depth to warrant a dedicated tutorial.
This tutorial connects familiar concept in HEP to best practices in Julia ecosystem. We go through the following topics:
• Simple curve fitting by minimizing Chi2, we use this as an opportunity...
A practical introduction into building forward models, how to perform Bayesian and maximum-likelihood inference on them using BAT, and how all of it relates to concepts of measure theory.
