Study of Mass Composition of Cosmic Rays with IceTop and IceCube. (arXiv:2107.09626v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Koundal_P/0/1/0/all/0/1">Paras Koundal</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Plum_M/0/1/0/all/0/1">Matthias Plum</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Saffer_J/0/1/0/all/0/1">Julian Saffer</a> (for the IceCube Collaboration)
The IceCube Neutrino Observatory is a multi-component detector at the South
Pole which detects high-energy particles emerging from astrophysical events.
These particles provide us with insights into the fundamental properties and
behaviour of their sources. Besides its principal usage and merits in neutrino
astronomy, using IceCube in conjunction with its surface array, IceTop, also
makes it a unique three-dimensional cosmic-ray detector. This distinctive
feature helps facilitate detailed cosmic-ray analysis in the transition region
from galactic to extragalactic sources. We will present the progress made on
multiple fronts to establish a framework for mass-estimation of primary cosmic
rays. The first technique relies on a likelihood-based analysis of the surface
signal distribution and improves upon the standard reconstruction technique.
The second uses advanced methods in graph neural networks to use the full
in-ice shower footprint, in addition to global shower-footprint features from
IceTop. A comparison between the two methods for composition analysis as well
as a possible extension of the analysis techniques for sub-PeV cosmic-ray
air-showers will also be discussed.
The IceCube Neutrino Observatory is a multi-component detector at the South
Pole which detects high-energy particles emerging from astrophysical events.
These particles provide us with insights into the fundamental properties and
behaviour of their sources. Besides its principal usage and merits in neutrino
astronomy, using IceCube in conjunction with its surface array, IceTop, also
makes it a unique three-dimensional cosmic-ray detector. This distinctive
feature helps facilitate detailed cosmic-ray analysis in the transition region
from galactic to extragalactic sources. We will present the progress made on
multiple fronts to establish a framework for mass-estimation of primary cosmic
rays. The first technique relies on a likelihood-based analysis of the surface
signal distribution and improves upon the standard reconstruction technique.
The second uses advanced methods in graph neural networks to use the full
in-ice shower footprint, in addition to global shower-footprint features from
IceTop. A comparison between the two methods for composition analysis as well
as a possible extension of the analysis techniques for sub-PeV cosmic-ray
air-showers will also be discussed.
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