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@PHDTHESIS{Glombitza:838783,
author = {Glombitza, Jonas},
othercontributors = {Erdmann, Martin and Wiebusch, Christopher},
title = {{D}eep-learning based measurement of the mass composition
of ultra-high energy cosmic rays using the surface detector
of the {P}ierre {A}uger {O}bservatory},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2022-00759},
pages = {1 Online-Ressource : Illustrationen},
year = {2021},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2022; Dissertation, RWTH Aachen University, 2021},
abstract = {Ultra-high energy cosmic rays (UHECRs) are the most
energetic particles found in nature. The search for their
origin and the determination of their mass composition is
still one of the biggest challenges of astroparticle
physics. When penetrating the Earth’s atmosphere, UHECRs
induce extensive air showers, which experiments like the
Pierre Auger Observatory can measure. The atmospheric depth
of the maximum of such showers, $X_{\mathrm{max}}$, contains
valuable information about the mass of the UHECR and can be
observed using Fluorescence Detectors (FDs), which feature a
limited duty cycle of roughly $15\\%$. In contrast, the
Surface Detector (SD) of the Pierre Auger Observatory
features a duty cycle of roughly $100\\%$, but can not
directly observe the shower maximum like the FD, making the
reconstruction a challenging task. In this thesis, a
measurement of the UHECR composition using the SD was
performed. For that, an algorithm for the reconstruction of
$X_{\mathrm{max}}$ using the time-dependent signals measured
by the SD was developed. The algorithm relies on deep
learning, the state-of-the-art machine learning approach
using deep neural networks and associated techniques. The
performance of the developed algorithm was extensively
studied on simulations, including various hadronic
interaction models. Additionally, the reconstruction of the
method was verified and calibrated using Auger hybrid data.
Subsequently, the energy evolution of $\langle
X_{\mathrm{max}} \rangle$ was measured from $3~\mathrm{EeV}$
to beyond $100~\mathrm{EeV}$. The measurement is in
excellent agreement with the results obtained using the
SD-based delta method and composition analyses performed
using the FD. The findings indicate a constant transition
from a lighter to a heavier composition with an elongation
rate of $D_{10}=25.8\pm 1.2~\mathrm{g/cm^{2}/decade}$. For
the first time, the energy evolution of
$\sigma(X_{\mathrm{max}})$, which is sensitive to the
composition mix, was determined from $3~\mathrm{EeV}$ to
beyond $100~\mathrm{EeV}$. In the common energy range at
lower energies, the results of the new method are in
remarkable agreement with the FD. At higher energies, the
obtained results indicate an increasingly heavy and pure
composition. This suggests that the observed cutoff in the
energy spectrum is caused by the fact that the cosmic-ray
accelerators reach their maximum energy.},
cin = {133110 / 133320 / 130000},
ddc = {530},
cid = {$I:(DE-82)133110_20140620$ / $I:(DE-82)133320_20140620$ /
$I:(DE-82)130000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2022-00759},
url = {https://publications.rwth-aachen.de/record/838783},
}
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