arXiv subjects

T. Stockmanns

Publications and source records attributed to T. Stockmanns.

4 recordsLinked to original sources

arXiv2020

A Generalized Approach to Longitudinal Momentum Determination in Cylindrical Straw Tube Detectors

The upcoming PANDA experiment at FAIR will be among a new generation of particle physics experiments to employ a novel event filtering system realised purely in software, i.e. a software trigger. To educate its triggering decisions, online reconstruction algorithms need to offer outstanding performance in terms of efficiency and track quality. We present a method to reconstruct longitudinal track parameters in PANDA's Straw Tube Tracker, which is general enough to be easily added to other track finding algorithms that focus on transversal reconstruction. For the pattern recognition part of this method, three approaches are employed and compared: A combinatorial path finding approach, a Hough transformation, and a recursive annealing fit. In a systematic comparison, the recursive annealing fit was found to outperform the other approaches in every category of quality parameters and reaches a reconstruction efficacy of 95% and higher.

physics.ins-det↗

arXiv2020

Measurement of proton-proton elastic scattering into the Coulomb region at $\text{P}_\text{beam}$ = 2.5, 2.8 and 3.2 GeV/c

The proton--proton elastic differential cross section at very small four momentum transfer squared has been measured at three different incident proton momenta in the range of 2.5 to 3.2 GeV/c by detecting the recoil proton at polar angles close to $90^\circ$. The measurement was performed at COSY with the KOALA detector covering the Coulomb-nuclear interference region. The total cross section $σ_\text{tot}$, which has been determined precisely, is consistent with previous measurements. The values of the slope parameter $B$ and the relative real amplitude ratio $ρ$ determined in this experiment alleviate the lack of data in the relevant energy region. This precise data on $ρ$ might be an important check for a new dispersion analysis.

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arXiv2019

Machine Learning for Track Finding at PANDA

We apply deep learning methods as a track finding algorithm to the PANDA Forward Tracking Stations (FTS). The problem is divided into three steps: The first step relies on an Artificial Neural Network (ANN) that is trained as a binary classifier to build track segments in three different parts of the FTS, namely FT1,FT2, FT3,FT4, and FT5,FT6. The ANN accepts hit pairs as an input and outputs a probability that they are on the same track or not. The second step builds 3D track segments from the 2D ones and is based on the geometry of the detector. The last step is to match the track segments from the different parts of the FTS to form a full track candidate, and is based on a Recurrent Neural Network (RNN). The RNN is used also as a binary classifier that outputs the probability that the combined track segments are a true track or not. The performance of the algorithm is judged based on the purity, efficiency and the ghost ratio of the reconstructed tracks. The purity specifies which fraction of hits in one track come from the correct particle. The correct particle is the particle, which produces the majority of hits in the track. The efficiency is defined as the ratio of the number of correctly reconstructed tracks to all generated tracks.

physics.ins-det↗

arXiv2006

Concept, realization and characterization of serially powered pixel modules (Serial Powering)

We prove and demonstrate here for the example of the large scale pixel detector of ATLAS that Serial Powering of pixel modules is a viable alternative and that has been devised and implemented for ATLAS pixel modules using dedicated on-chip voltage regulators and modified flex hybrids circuits. The equivalent of a pixel ladder consisting of six serially powered pixel modules with about 0.3Mpixels has been built and the performance with respect to noise and threshold stability and operation failures has been studied. We believe that Serial Powering in general will be necessary for future large scale tracking detectors.

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