Study of a data analysis method for the angle resolving silicon telescope

Žugec, P.; Barbagallo, M.; Andrzejewski, J.; Perkowski, J.; Colonna, N.; Bosnar, D.; Gawlik, A.; Sabaté-Gilarte, M.; Bacak, M.; Mingrone, F.; Chiaveri, E.; Šako, M.

doi:10.1088/1748-0221/15/02/p02011

Cited by 4 publications

(2 citation statements)

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“…This measurement was motivated by the unexpected results from an earlier integral measurement of the 12 C(n, p) reaction [17,18], yielding an integral cross section higher than indicated by any past dataset. The analysis of the data from the latest 12 C(n, p) and 12 C(n, d) measurements is under way, and the special analysis procedure has already been developed in order to properly take into account the challenging nature of these reactions [19]. The geometric configuration of the used detector setup is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

A synchronization method for the multi-channel silicon telescope

Žugec,

Barbagallo,

Andrzejewski

et al. 2020

Preprint

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A simple method is presented for the simultaneous off-line synchronization of the digitally recorded data-streams from a multichannel silicon telescope. The method is based both on the synchronization between the separate pairs of silicon strips and on the synchronization relative to an external timing device. Though only a reduced subset of these constraints is necessary in ideal circumstances, it is shown that this minimal set of conditions may not be sufficient for adequate synchronization in all cases. All available sources of information are therefore considered, in order to constrain the final synchronization as well as possible.

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Section: Methodsmentioning

confidence: 99%

A synchronization method for the multi-channel silicon telescope

Žugec,

Barbagallo,

Andrzejewski

et al. 2020

Preprint

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“…In order to correct for this in the offline analysis, a simple but effective synchronization method has been developed [13]. Furthermore, due to the challenging features of the nat C(n,p) and nat C(n,d) reactions -in particular the sensitivity to excited states in daughter nuclei and the expected anisotropy in angular distributions of the reaction products -a special method was developed for the analysis of the experimental data, aiming at the optimal extraction of energy-differential cross sections [17]. What remains is a classification of the experimental counts according to the reaction type (i.e.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based event classification for the energy-differential measurement of the $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reactions

Žugec,

Barbagallo,

Andrzejewski

et al. 2022

Preprint

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The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint nat C(n,p) and nat C(n,d) reaction cross section measurement from the neutron time of flight facility n TOF at CERN. Each relevant ∆E-E pair of strips from two segmented silicon telescopes is treated separately and afforded its own dedicated neural network. An important part of the procedure is a careful preparation of training datasets, based on the raw data from Geant4 simulations. Instead of using these raw data for the training of neural networks, we divide a relevant 3-parameter space into discrete voxels, classify each voxel according to a particle/reaction type and submit these voxels to a training procedure. The classification capabilities of the structurally optimized and trained neural networks are found to be superior to those of the manually selected cuts.

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