scholarly journals Improvement in fast particle track reconstruction with robust statistics

Author(s):  
M.G. Aartsen ◽  
R. Abbasi ◽  
Y. Abdou ◽  
M. Ackermann ◽  
J. Adams ◽  
...  
2020 ◽  
Vol 15 (09) ◽  
pp. P09030-P09030
Author(s):  
S. Lantz ◽  
K. McDermott ◽  
M. Reid ◽  
D. Riley ◽  
P. Wittich ◽  
...  

2021 ◽  
Author(s):  
Pavel Goncharov ◽  
Egor Schavelev ◽  
Anastasia Nikolskaya ◽  
Gennady Ososkov

2019 ◽  
Author(s):  
Dmitriy Baranov ◽  
Pavel Goncharov ◽  
Gennady Ososkov ◽  
Egor Shchavelev

2021 ◽  
Vol 11 (1) ◽  
pp. 440
Author(s):  
Johannes Leidner ◽  
Fabrizio Murtas ◽  
Marco Silari

The GEMPix is a small gaseous detector with a highly pixelated readout, consisting of a drift region, three Gas Electron Multipliers (GEMs) for signal amplification, and four Timepix ASICs with 55 µm pixel pitch and a total of 262,144 pixels. A continuous flow of a gas mixture such as Ar:CO2:CF4, Ar:CO2 or propane-based tissue equivalent gas is supplied externally at a rate of 5 L/h. This article reviews the medical applications of the GEMPix. These include relative dose measurements in conventional photon radiation therapy and in carbon ion beams, by which on-line 2D dose images provided a similar or better performance compared to gafchromic films. Depth scans in a water phantom with 12C ions allowed measuring the 3D energy deposition and reconstructing the Bragg curve of a pencil beam. Microdosimetric measurements performed in neutron and photon fields allowed comparing dose spectra with those from Tissue Equivalent Proportional Counters and, additionally, to obtain particle track images. Some preliminary measurements performed to check the capabilities as the detector in proton tomography are also illustrated. The most important on-going developments are: (1) a new, larger area readout to cover the typical maximum field size in radiation therapy of 20 × 20 cm2; (2) a sealed and low-pressure version to facilitate measurements and to increase the equivalent spatial resolution for microdosimetry; (3) 3D particle track reconstruction when operating the GEMPix as a Time Projection Chamber.


2019 ◽  
Vol 214 ◽  
pp. 02026
Author(s):  
Michael Papenbrock

The future PANDA experiment at FAIR experiment aims to cover a wide range of processes in antiproton-proton collisions at event rates of up to 20 MHz. Such event rates make reconstruction a challenging task for the purely software-based event filter. Investigating complex event topologies with displaced vertices increases the difficulty even further. Here we present two attempts to meet these future challenges: an algorithm for track reconstruction based on pattern matching with pre-determined look-up tables, and as a continuation of this approach a system of neural networks for identifying specific particle track candidates and predicting their momentum.


2020 ◽  
Vol 245 ◽  
pp. 09013 ◽  
Author(s):  
Cenk Tüysüz ◽  
Federico Carminati ◽  
Bilge Demirköz ◽  
Daniel Dobos ◽  
Fabio Fracas ◽  
...  

Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.


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