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Published January 22, 2024 | Version v1

Study of the PMTs signals during the first underground run of the LIME prototype for the CYGNO experiment

Authors/Creators

  • 1. U Rome La Sapienza main
  • 2. borra1763039studentiuniroma1it

Contributors

  • 1. U Rome La Sapienza main

Description

This thesis focuses on the commissioning and the data analysis of the LIME prototype of the CYGNO experiment, which aims at developing a detector for direct detection of dark matter. The LIME prototype was installed this year (2022) at Laboratori Nazionali del Gran Sasso, in order to characterize the detector response in the underground environment and in site background. The CYGNO experiment aims at exploiting the optical readout approach of multiple-GEM structures in large volume gaseous Time Projection Chambers (TPCs) for the study of rare events as interactions of low mass DM or solar neutrinos. The combined use of high-granularity sCMOS cameras and fast light sensors allows the reconstruction of the 3D direction of the tracks, offering good energy resolution and very high sensitivity in the few keV energy range. During the underground commissioning, my main contributions were to help improve the data acquisition system and characterize PMTs response. The main goal of the analysis on the PMTs waveforms was to reconstruct the event position and energy. Having these quantities it is possible to match the PMTs signals with the sCMOS sensor pictures. Utilizing the charge collected by the 4 PMTs it is in fact possible to reconstruct the spot position on the GEMs plane and the light of the event. After a loose selection on the waveforms, a Bayesian fit was performed to fit the (x, y) position and the light L, produced after the amplification stage of the detector. The position and light are reconstructed with 10% and 11% resolution respectively, and then compared with the picture-reconstructed variables, obtaining encouraging results.

Files

CERN-THESIS-2023-323.pdf

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Additional details

Identifiers

CDS
2887283
CDS Report Number
CERN-THESIS-2023-323

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