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.
Submitted by firstname.lastname@example.org on Wed, 09/02/2022 - 18:24.
The PANDA experiment at FAIR offers unique possibilities for performing hyperon physics.
The detector will enable the reconstruction of both hyperon and antihyperon, which will
be created together in proton-antiproton collisions. This enables investigations of the strong
interaction in the non-perturbative regime. Due to their relatively long-lived nature, the hyperons
impose a particular challenge on the track reconstruction and event building. In order to
exploit the large expected reaction rates to the fullest, PANDA will utilize a fully software-based
event filtering. Therefore, reconstructing hyperons for such a filter requires online track
reconstruction that can handle particles created a measurable distance away from the interaction
point and, at the same time, operate on free streaming data is needed. Until antiprotons are
available at PANDA, a part of the hyperon program can be carried out with the predecessor,
PANDA@HADES using a proton beam.
In this thesis, investigations of the detector signatures from the decay channels Λ → pπ-, Ξ- →
Λπ- and Ω- → Λ K- produced in YbarY reactions are presented. The detector signatures guide
the subsequent track reconstruction algorithms. A candidate for online track reconstruction
algorithms on free streaming data based on a 4D Cellular Automaton has been developed and is
benchmarked. It utilizes information from the PANDA straw tube tracker and is agnostic to the
point of origin of the particle. The track reconstruction quality assurance procedure and results
from the tracking at different event rates are also presented. Finally, extrapolation algorithms
for including hit information from additional detectors in the tracks are outlined.
In order to maximize the potential of the predecessor experiment PANDA@HADES, a
kinematic fitting procedure has been developed for HADES that combines geometric the decay
vertex information of neutral particles and track parameters such as momentum. Benchmark
studies on simulated data from the channel p(3.5 GeV)p → ΛK+p are presented as well as tests
of the kinematic fit on experimental data from 2007.