This tutorial will cover how to optimise various aspects of analyses -- such as cuts, binning, and learned observables like neural networks -- using gradient-based optimisation. This has been made possible due to work on the [relaxed][1] software package, which offers a set of standard HEP operations that have been made differentiable.
In addition to targeting Asimov significance, we...
In the ideal world, we describe our models with recognizable mathematical expressions and directly fit those models to large data samples with high performance. It turns out that this can be done by formulating our models with SymPy (a Computer Algebra System) and using its symbolic expression trees as template to computational back-ends like JAX and TensorFlow. The...
Pythia is one of the most widely used general-purpose Monte Carlo event generators in HEP. It has included a python interface to the underlying C++ since v8.219, and it was redesigned to handle C++11 using pyBind11 since v8.301, allowing users to generate a custom python interface.
This talk will showcase the power and flexibility of Pythia's default, simplified python interface by...
The IRIS-HEP Analysis Grand Challenge (AGC) provides an environment for investigating analysis methods in the context of a realistic physics analysis. It features an analysis task that captures all relevant workflow aspects encountered in LHC analyses, reaching from data delivery to statistical inference. By using publicly available Open Data, the AGC allows anyone interested to test different...
This Jupyter notebook tutorial will cover usage of Awkward Arrays within an RDataFrame.
In Awkward Array version 2, the ak.to_rdataframe function presents a view of an Awkward Array as an RDataFrame source. This view is generated on demand and the data is not copied. The column readers are generated based on the run-time type of the views. The readers are passed to a generated source...
The NOvA collaboration together with a Dept. of Energy ASCR supported SciDAC-4 project, have been exploring Python-based analysis workflows for HPC platforms. This research has been focused on adapting machine-learning application workflows using highly-parallel computing environments for neutrino-nucleon cross section measurements. This work accelerates scientific analysis and lowers the...
