Speaker
Description
One of the central tools in hadron spectroscopy is amplitude analysis (partial-wave analysis) to interpret the experimental data. Amplitude models are fitted to data with large statistics to extract information about resonances and branching fractions. In amplitude analysis, we require flexibility to implement models with different decay hypotheses, spin formalisms, and resonance parametrisations, but also require computational performance to quickly fit the models to large datasets.
Computational performance can nowadays easily be achieved with the use of array-oriented libraries like JAX, TensorFlow, and Numba, which allow users to write backend-agnostic code for different types of accelerators like GPUs and multithreaded CPUs. The ComPWA project provides an additional layer of flexibility by formulating amplitude models with a Computer Algebra System (CAS) and using the expression trees to generate array-oriented code for multiple libraries. In addition, the use of a CAS results in a transparent, self-documenting workflow that further bridges the gap between theory and code.
Significance
We show how the combination of a CAS and array-oriented libraries result in a powerful workflow that opens the door to different fitting strategies that are better suited than gradient-descent algorithms for a multidimensional parameter spaces. We show how array-oriented libraries efficiently use the computational accelerator and its memory, resulting in much more performant fits. The combination also makes it possible to perform other numerical studies, such as the computation of a vector field (for polarimetry) rather than an intensity distribution.
References
Most recent talk about ComPWA: https://indico.jlab.org/event/739/contributions/14325
Polarimetry publication: https://doi.org/10.1007/JHEP07%282023%29228
