Detector software

PANDA Annual Report 2023

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones.

The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the $\mathrm{\overline{P}}$ANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of $J^{PC}=1^{-+}$ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles.

The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio $\mathrm{F_{LU}^{sin(\phi)}/F_{UU}}$ has been performed for K$^\mathrm{\pm}$, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. $\mathrm{F_{LU}^{sin(\phi)}}$ is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of $z$, $x_B$, $P_T$ and $Q^2$ with virtualities $Q^2$ ranging from 1 GeV$^2$ up to 8 GeV$^2$ using machine learning techniques for improved particle identification.

A new generation of experiments is being developed, where the challenge of separating rare signal processes from background at high intensities requires a change
of trigger paradigm. At the future PANDA experiment at FAIR, hardware triggers will be abandoned and instead a purely software-based system will be used.
This requires novel reconstruction methods with the ability to process data from
many events simultaneously.
A 4D tracking algorithm based on the cellular automaton has been developed which will utilize the timing information from detector signals. Simulation studies
have been performed to test its performance on the foreseen free-streaming data from the PANDA detector. For this purpose, a quality assurance procedure for
tracking on free-streaming data was implemented in the PANDA software. The studies show that at higher interaction rates, 4D tracking performs better than
the 3D algorithm in terms of efficiency, 84% compared to 77%. The fake track suppression is also greatly improved, compared to the 3D tracking with roughly
a 50% decrease in the ghost rate.

The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at FAIR (Facility for Anti-proton and Ion Research) aims to study strong interactions in the confinement domain. In PANDA, a continuous beam of anti-protons will impinge on a fixed hydrogen target inside the High Energy Storage Ring (HESR), a feature intended to attain high interaction rates for various physics studies e.g. hyperon production.

This thesis addresses the challenges of running PANDA under realistic conditions. The focus is two-fold: developing deep learning methods to reconstruct particle trajectories and reconstruct hyperons using realistic target profiles. Two approaches are used: (i) standard deep learning model such as dense network, and (ii) geometric deep leaning model such as interaction graph neural networks. The deep learning methods have given promising results, especially when it comes to (i) reconstruction of low-momentum particles that frequently occur in hadron physics experiments and (ii) reconstruction of tracks originating far from the interaction point. Both points are critical in many hyperon studies. However, further studies are needed to mitigate e.g. high clone rate. For the realistic target profiles, these pioneering simulations address the effect of residual gas on hyperon reconstruction. The results have shown that the signal-to-background ratio becomes worse by about a factor of 2 compared to the ideal target, however, the background level is still sufficiently low for these studies to be feasible. Further improvements can be made on the target side to achieve a better vacuum in the beam pipe and on the analysis side to improve the event selection.

Finally, solutions are suggested to improve results, especially for the geometric deep learning method in handling low-momentum particles contributing to the high clone rate. In addition, a better way to build ground truth can improve the performance of our approach.

Presentation of the build & deployment mechanisms forseen for the DCS software system within PANDA at the Build and Deployment Workshop at the EPICS Collaboration Meeting.