Speaker
Description
The sustainable use of energy should be fulfilled as well in the field of particle physics, especially considering that it has additional advantages. Over recent decades, the continuous increase of experimental data in high energy physics applications has led to a significant computational demand. In particular, time-consuming coupled channel analyses require sometimes fits with a hundred free parameters that extend over several weeks, are an area where such optimisation is of great value. To address this need, we are currently integrating various AI tools into PAWIAN (PArtial Wave Interactive ANalysis), a software package developed at Ruhr-University Bochum for conducting partial wave analysis even more efficiently. PAWIAN's architecture enables the simultaneous analysis of data from various hadron physics experiments and supports sophisticated dynamical models as K-matrix formalism or tensor formalism. The current project includes efficiency improvements within the minimisation procedures by moving from numerical methods towards automatic differentiation for the intermediate derivative computations. Additionally, augmenting the gradient descent algorithm with a velocity term aims to address issues such as local minima, instabilities, and prolonged computation time. Furthermore, we are working on pseudo-event binning, where events are grouped to optimise the number of function evaluations without losing precision. Preliminary results of these efforts and specific benchmark cases regarding the implementation of these AI techniques will be presented.
