SearcharxivSearch

arXiv subjects

Tobias Jenegger

Publications and source records attributed to Tobias Jenegger.

2 recordsLinked to original sources

Charge sharing and alignment performance of bent ALPIDEs measured with low-energy protons

The upgrade of the ALICE experiments Inner Tracking System (ITS3) aims to replace its innermost detection layers with bent wafer-scale CMOS MAPS sensors. This study examines the performance of ALPIDE chips, currently used in the ALICE ITS2, when operated in a bent configuration under realistic experimental conditions. Proton beams with energies of 80 MeV, 120 MeV and 200 MeV were used to study proton-proton elastic scattering on a polypropylene fiber target reconstructed using two opposing arms of trackers with sensors bent to radii of 18 mm, 24 mm and 30 mm. The measured low-momentum protons provided a testbed for investigating clustering behavior in high-energy loss events, where no significant impact of bending was observed on cluster size. Additionally, alignment strategies for bent detectors were evaluated using the distance of closest approach (DCA) and opening angle between scattered proton tracks as benchmarks. The achieved resolution matches expectations from simulations, confirming the suitability of bent MAPS sensors for future high-energy and nuclear physics applications.

physics.ins-det

Machine Learning for the Cluster Reconstruction in the CALIFA Calorimeter at R3B

The R3B experiment at FAIR studies nuclear reactions using high-energy radioactive beams. One key detector in R3B is the CALIFA calorimeter consisting of 2544 CsI(Tl) scintillator crystals designed to detect light charged particles and gamma rays with an energy resolution in the per cent range after Doppler correction. Precise cluster reconstruction from sparse hit patterns is a crucial requirement. Standard algorithms typically use fixed cluster sizes or geometric thresholds. To enhance performance, advanced machine learning techniques such as agglomerative clustering were implemented to use the full multi-dimensional parameter space including geometry, energy and time of individual interactions. An Edge Detection Neural Network exhibited significant differences. This study, based on Geant4 simulations, demonstrates improvements in cluster reconstruction efficiency of more than 30%, showcasing the potential of machine learning in nuclear physics experiments.

physics.ins-det